Ultrasound image segmentation: a survey <s> C. Priors 1) Image Features: <s> B-mode texture characterization by supervised methods of pattern recognition is subject to the following drawbacks: precise localization of the lesion to characterize is often difficult and, even when the lesion is well isolated, its texture can be corrupted by the presence of tumor non specific structures. These structures are not easily discernable and introduce a bias in the statistical measures. The results presented in this paper show that these problems can be circumvented by the use of an unsupervised method of image segmentation. The method enhances the B-mode image and partitions the non specific structures and the lesion texture in different regions which can be characterized independently by statistical methods. The unsupervised approach also facilitates the clinical diagnosis done by visual inspection, by revealing subtle characteristics of the B-mode textures. <s> BIB001 </s> Ultrasound image segmentation: a survey <s> C. Priors 1) Image Features: <s> An approach to ultrasonic tissue characterization, using textural features of the B-scan image, is described. Portions of a B-scan image, 64 X 64 pixels spatially by 8 bits deep, are acquired from regions of interest and subjected to computer analysis. A systematic approach to defining a set of 93 textural features of a B-scan is described and methods and criteria for selecting optimum combinations of these are discussed. As a test of its power, the approach has been applied to the discrimination between the B-scan textures corresponding to livers and spleens of normal humans and various measures of "success" have been quantified both on a "training set only" and on a "training set plus test set" basis. The overall test probability of success of 82% on a single image and 94% on a subject yielding multiple images indicates the potential of the techniques for conditions where a subtle but uniform change in parenchymal texture may be present. <s> BIB002 </s> Ultrasound image segmentation: a survey <s> C. Priors 1) Image Features: <s> An automatic algorithm has been developed for high-speed detection of cavity boundaries in sequential 2-D echocardiograms using an optimization algorithm called simulated annealing (SA). The algorithm has three stages. (1) A predetermined window of size nxm is decimated to size n'xm' after low-pass filtering. (2) An iterative radial gradient algorithm is employed to determine the center of gravity (CG) of the cavity. (3) 64 radii which originate from the CG defined in stage 2 are bounded by the high-probability region. Each bounded radius is defined as a link in a 1-D, 64-member cyclic Markov random field. This algorithm is unique in that it compounds spatial and temporal information along with a physical model in its decision rule, whereas most other algorithms base their decisions on spatial data alone. This is the first implementation of a relaxation algorithm for edge detection in echocardiograms. Results attained using this algorithm on real data have been highly encouraging. <s> BIB003 </s> Ultrasound image segmentation: a survey <s> C. Priors 1) Image Features: <s> The classification of ultrasonic liver images is studied, making use of the spatial gray-level dependence matrices, the Fourier power spectrum, the gray-level difference statistics, and the Laws texture energy measures. Features of these types are used to classify three sets of ultrasonic liver images-normal liver, hepatoma, and cirrhosis (30 samples each). The Bayes classifier and the Hotelling trace criterion are employed to evaluate the performance of these features. From the viewpoint of speed and accuracy of classification, it is found that these features do not perform well enough. Hence, a new texture feature set (multiresolution fractal features) based on multiple resolution imagery and the fractional Brownian motion model is proposed to detect diffuse liver diseases quickly and accurately. Fractal dimensions estimated at various resolutions of the image are gathered to form the feature vector. Texture information contained in the proposed feature vector is discussed. A real-time implementation of the algorithm produces about 90% correct classification for the three sets of ultrasonic liver images. > <s> BIB004 </s> Ultrasound image segmentation: a survey <s> C. Priors 1) Image Features: <s> As speckle on ultrasonic B-scan may reveal information relative to tissue structure, the present work attempts to discriminate the various prostatic tissues (normal tissue, benign prostatic hypertrophy and cancer) by means of texture analysis. We select regions of interest by their homogeneous appearance. A pre-processing stage is required to obtain stationary samples. The method used measures the second-order statistics, namely co-occurrence matrices. Fairly good tissue signatures have been obtained with parameters derived from these matrices. Of 37 images processed, 78 percent of the samples were classified with success, which is a high score considering that the images cannot be discriminated visually. However, while such results are obtained when wide regions of interest are investigated (64 x 64 pixels), they are not as good with smaller sample sizes, i.e., when the pathological area is very small. <s> BIB005 </s> Ultrasound image segmentation: a survey <s> C. Priors 1) Image Features: <s> A multiresolution texture segmentation (MTS) approach to image segmentation that addresses the issues of texture characterization, image resolution, and time to complete the segmentation is presented. The approach generalizes the conventional simulated annealing method to a multiresolution framework and minimizes an energy function that is dependent on the resolution of the size of the texture blocks in an image. A rigorous experimental procedure is also proposed to demonstrate the advantages of the proposed MTS approach on the accuracy of the segmentation, the efficiency of the algorithm, and the use of varying features at different resolution. Semireal images, created by sampling a series of diagnostic ultrasound images of an ovary in vitro, were tested to produce statistical measures on the performance of the approach. The ultrasound images themselves were then segmented to determine if the approach can achieve accurate results for the intended ultrasound application. Experimental results suggest that the MTS approach converges faster and produces better segmentation results than the single-level approach. > <s> BIB006 </s> Ultrasound image segmentation: a survey <s> C. Priors 1) Image Features: <s> To improve the ability of ultrasound to distinguish benign from malignant breast lesions, we used quantitative analysis of ultrasound image texture. Eight cancers, 22 cysts, 28 fibroadenomata, and 22 fibrocystic nodules were studied. The true nature of each lesion was determined by aspiration (for some cysts) or by open biopsy. Analysis of image texture was performed on digitized video output from the ultrasound scanner using fractal analysis and statistical texture analysis methods. The most useful features were those derived from co-occurrence matrices of the images. Using two features together (contrast of a co-occurrence matrix taken in an oblique direction, and correlation of a co-occurrence matrix taken in the horizontal direction), it was possible to exclude 78% of fibroadenomata, 73% of cysts, and 91% of fibrocystic nodules while maintaining 100% sensitivity for cancer. These findings suggest that ultrasonic image texture analysis is a simple way to markedly reduce the number of benign lesion biopsies without missing additional cancers. <s> BIB007 </s> Ultrasound image segmentation: a survey <s> C. Priors 1) Image Features: <s> Radio frequency (RF) echograms were acquired from human subjects without liver pathology (n = 126), who were in the range of 20 to 84 years of age. After appropriate correction for the equipment settings and performance characteristics, acoustospectrographic parameters were estimated. The data were corrected for the frequency-dependent attenuation and then software demodulated. Image texture parameters were calculated based on the assumption of a particular tissue model, and purely statistical parameters were also estimated. The data thus obtained from each subject were corrected for the age trend that was assessed in an earlier publication by the authors. A total of 18 parameters was considered and the correlations were analyzed. It is concluded that nine parameters could be identified which did not strongly correlate with each other. This conclusion is supported by analysis of correlation data from patients. In a companion paper, a discriminant analysis is reported, based on the selected parameters (nine) and applied to the differentiation between normals and various classes of diffuse liver disease. <s> BIB008 </s> Ultrasound image segmentation: a survey <s> C. Priors 1) Image Features: <s> This paper describes a practical method for automated determination of the contour of the prostate in ultrasonographic images. In this method, we use specific edge detection techniques, based on nonlinear Laplace filtering. Possible edges are located at zero-crossings of the second derivative of the image. The strength of the edge is reflected by the value of the gradient of the image at that location. Combining the information about location and strength, an edge intensity image is constructed from the initial ultrasonographic image. In our method, edge enhancement techniques are performed on the edge intensity image. Edges that actually represent a boundary are selected and linked: interpolation techniques are used to fill the gaps between detected boundary edges. The method for contour determination in ultrasonographic images is used for accurate volume measurements in an everyday clinical environment. Its computer implementation is fast, accurate (mean difference within 6% of exact volume) and easy to use. <s> BIB009 </s> Ultrasound image segmentation: a survey <s> C. Priors 1) Image Features: <s> The covariance matrices associated with each state of health or disease from a previous study are used as the basis of an image staining display technique for aid in quantitative differential diagnosis. A state of health or disease is chosen by the clinician: this selects the covariance matrix from the data base. A region of interest (ROI) is then scrolled through an abdominal B-scan. For each position of the ROI a point in the four-dimensional feature space is calculated. A natural measure of the distance of this point from the center of mass (multivariate mean) of the disease class is calculated in terms of the covariance matrix of this class; this measure is the Mahalanobis distance. The confidence level for acceptance or rejection of the hypothesized disease class is obtained from the probability distribution of this distance, the T(2) probability law. This confidence level is color coded and used as a color stain that overlays the original scan at that position. The variability of the calculated features is studied as a function of ROI size, or the spatial resolution of the color coded image, and it is found that for an ROI in the neighborhood of 4 cm(2) most of the variability due to the finite number of independent samples (speckles) is averaged out, leaving the "noise floor" associated with inter- and intra-patient variability. ROIs on the order of 1 cm(2) may result with technical advances in B-scan resolution. A small number of points on organ boundaries are entered by the user, to fit with arcs of ellipses to be used to switch between organ (liver and kidney) data bases as the ROI encounters the boundary. By selecting in turn various state-of-health or state-of-disease databases, such images of confidence levels may be used for quantitative differential diagnosis. The method is not limited to ultrasound, being applicable in principle to features obtained from any modality or multimodality combination. <s> BIB010 </s> Ultrasound image segmentation: a survey <s> C. Priors 1) Image Features: <s> We propose a novel method for obtaining the maximum a posteriori (MAP) probabilistic segmentation of speckle-laden ultrasound images. Our technique is multiple-resolution based, and relies on the conversion of speckle images with Rayleigh statistics to subsampled images with Gaussian statistics. This conversion reduces computation time, as well as allowing accurate parameter estimation for a probabilistic segmentation algorithm. Results appear to provide improvements over previous techniques in terms of low-contrast detail and accuracy. <s> BIB011 </s> Ultrasound image segmentation: a survey <s> C. Priors 1) Image Features: <s> A computational method is reported which allows the fully automated recovery of the three-dimensional shape of the cardiac left ventricle from a reduced set of apical echo views. Two typically ill-posed problems have been faced: 1) the detection of the left ventricle contours in each view, and 2) the integration of the detected contour points (which form a sparse and partially inconsistent data set) into a single surface representation. The authors' solution to these problems is based on a careful integration of standard computer vision algorithms with neural networks. Boundary detection comprises three steps: edge detection, edge grouping, and edge classification. The first and second steps (which are typical early-vision tasks not involving specific domain-knowledge) have been performed through fast, well-established algorithms of computer vision. The higher level task of left ventricle-edge discrimination, which involves the exploitation of specific knowledge about the left ventricle silhouette, has been performed by feedforward neural networks. Following the most recent results in the field of computer vision, the first step in solving the problem of recovering the ventricle surface has been the adoption of a physically inspired model of it. Basically, the authors have modeled the left ventricle surface as a closed, thin, elastic surface and the data as a set of radial springs acting on it. The recovery process is equivalent to the settling of the surface-plus-springs system into a stable configuration of minimum potential energy. The finite element discretization of this model leads directly to an analog neural-network implementation. The efficiency of such an implementation has been remarkably enhanced through a learning algorithm which embeds specific knowledge about the shape of the left ventricle in the network. Experiments using clinical echographic sequences are described. Four apical views (each with a different rotation of the probe) have been acquired during a heartbeat from a set of seven normal subjects. These images have been utilized to set the various processing modules and test their capabilities. > <s> BIB012 </s> Ultrasound image segmentation: a survey <s> C. Priors 1) Image Features: <s> Intravascular ultrasound imaging of coronary arteries provides important information about coronary lumen, wall, and plaque characteristics. Quantitative studies of coronary atherosclerosis using intravascular ultrasound and manual identification of wall and plaque borders are limited by the need for observers with substantial experience and the tedious nature of manual border detection. We have developed a method for segmentation of intravascular ultrasound images that identifies the internal and external elastic laminae and the plaque-lumen interface. The border detection algorithm was evaluated in a set of 38 intravascular ultrasound images acquired from fresh cadaveric hearts using a 30 MHz imaging catheter. To assess the performance of our border detection method we compared five quantitative measures of arterial anatomy derived from computer-detected borders with measures derived from borders manually defined by expert observers. Computer-detected and observer-defined lumen areas correlated very well (r=0.96, y=1.02x+0.52), as did plaque areas (r=0.95, y=1.07x-0.48), and percent area stenosis (r=0.93, y=0.99x-1.34.) Computer-derived segmental plaque thickness measurements were highly accurate. Our knowledge-based intravascular ultrasound segmentation method shows substantial promise for the quantitative analysis of in vivo intravascular ultrasound image data. <s> BIB013 </s> Ultrasound image segmentation: a survey <s> C. Priors 1) Image Features: <s> !, Model-based vision is firmly established as a robust approach to recognizing and locating known rigid objects in the presence of noise, clutter, and occlusion. It is more problematic to apply modelbased methods to images of objects whose appearance can vary, though a number of approaches based on the use of flexible templates have been proposed. The problem with existing methods is that they sacrifice model specificity in order to accommodate variability, thereby compromising robustness during image interpretation. We argue that a model should only be able to deform in ways characteristic of the class of objects it represents. We describe a method for building models by learning patterns of variability from a training set of correctly annotated images. These models can be used for image search in an iterative refinement algorithm analogous to that employed by Active Contour Models (Snakes). The key difference is that our Active Shape Models can only deform to fit the data in ways consistent with the training set. We show several practical examples where we have built such models and used them to locate partially occluded objects in noisy, cluttered images. Q 199s A&& prrss, IN. <s> BIB014 </s> Ultrasound image segmentation: a survey <s> C. Priors 1) Image Features: <s> Tracing of left-ventricular epicardial and endocardial borders on echocardiographic sequences is essential for quantification of cardiac function. The authors designed a method based on an extension of active contour models to detect both epicardial and endocardial borders on short-axis cardiac sequences spanning the entire cardiac cycle. They validated the results by comparing the computer-generated boundaries to the boundaries manually outlined by four expert observers on 44 clinical data sets. The mean boundary distance between the computer-generated boundaries and the manually outlined boundaries was 2.80 mm (/spl sigma/=1.28 mm) for the epicardium and 3.61 (/spl sigma/=1.68 mm) for the endocardium. These distances were comparable to interobserver distances, which had a mean of 3.79 mm (/spl sigma/=1.53 mm) for epicardial borders and 2.67 mm (/spl sigma/=0.88 mm) for endocardial borders. The correlation coefficient between the areas enclosed by the computer-generated boundaries and the average manually outlined boundaries was 0.95 for epicardium and 0.91 for endocardium. The algorithm is fairly insensitive to the choice of the initial curve. Thus, the authors have developed an effective and robust algorithm to extract left-ventricular boundaries from echocardiographic sequences. <s> BIB015 </s> Ultrasound image segmentation: a survey <s> C. Priors 1) Image Features: <s> Conventional two-dimensional (2-D) texture parameters serve as the "gold standard" of texture analysis. The authors compared a new stochastic model, based on autoregressive periodic random field models (APRFM) with conventional texture analysts (CTA) parameter, which were defined as measures of the co-occurrence matrix, i.e., entropy, contrast, correlation, uniformity, and maximum frequency. By fitting the model to a given texture pattern, the estimated model parameters are suitable texture features. In 81 patients, divided into patients without (N=19) and with (N=62) microfocal lesions of the liver, a set of 24 CTA and 16 APRFM parameters were calculated from ultrasonic liver images. To ensure simple computation the APRFM parameters were based on the unilateral type of pixel neighborhood. Regenerated texture by APRFM was visually comparable with the original texture. Reclassification analysis using the classification and regression tree (CART) discriminant analysis system and the area under the receiver operating characteristic (ROC) curve was used to assess the texture classification potency of APRFM- and CTA-parameters. Discriminating between liver with and without microfocal lesions, the best results were seen for the APRFM parameter. <s> BIB016 </s> Ultrasound image segmentation: a survey <s> C. Priors 1) Image Features: <s> Visual criteria for diagnosing diffused liver diseases from ultrasound images can be assisted by computerized tissue classification. Feature extraction algorithms are proposed in this paper to extract the tissue characterization parameters from liver images. The resulting parameter set is further processed to obtain the minimum number of parameters which represent the most discriminating pattern space for classification. This preprocessing step has been applied to over 120 distinct pathology-investigated cases to obtain the learning data for classification. The extracted features are divided into independent training and test sets, and are used to develop and compare both statistical and neural classifiers. The optimal criteria for these classifiers are set to have minimum classification error, ease of implementation and learning, and the flexibility for future modifications. Various algorithms of classification based on statistical and neural network methods are presented and tested. The authors show that very good diagnostic rates can be obtained using unconventional classifiers trained on actual patient data. <s> BIB017 </s> Ultrasound image segmentation: a survey <s> C. Priors 1) Image Features: <s> Ultrasound imaging is a powerful tool for characterizing the state of soft tissues; however, in some cases, where only subtle differences in images are seen as in certain liver lesions such as hemangioma and malignancy, existing B-scan methods are inadequate. More detailed analyses of image texture parameters along with artificial neural networks can be utilized to enhance differentiation. From B-scan ultrasound images, 11 texture parameters comprising of first, second and run length statistics have been obtained for normal, hemangioma and malignant livers. Tissue characterization was then performed using a multilayered backpropagation neural network. The results for 113 cases have been compared with a classification based on discriminant analysis. For linear discriminant analysis, classification accuracy is 79.6% and with neural networks the accuracy is 100%. The present results show that neural networks classify better than discriminant analysis, demonstrating a much potential for clinical application. <s> BIB018 </s> Ultrasound image segmentation: a survey <s> C. Priors 1) Image Features: <s> Segmenting two-dimensional images of the prostate into prostate and nonprostate regions is required when forming a three-dimensional image of the prostate from a set of parallel two-dimensional images. The texture-based segmentation method presented here is a pixel classifier based on four texture energy measures associated with each pixel in the image. An automated clustering procedure is used to label each pixel in the image with the label of its most probable class. The segmented images produced as the result of applying the algorithm to an example image are presented and discussed. The automated segmentation algorithm has been found to hold promise as an automated segmentation method. <s> BIB019 </s> Ultrasound image segmentation: a survey <s> C. Priors 1) Image Features: <s> Abstract One prerequisite for standard clinical use of intravascular ultrasound imaging is rapid evaluation of the data. The main quantities to be extracted from the data are the size and the shape of the lumen. Until now, no accurate, robust and reproducible method to obtain the lumen boundaries from intravascular ultrasound images has been described. In this study, 21 different (semi-)automated binary-segmentation methods for determining the lumen are compared with manual segmentation to find an alternative for the laborious and subjective procedure of manual editing. After a preprocessing step in which the catheter area is filled with lumen-like grey values, all approaches consist of two steps: (i) smoothing the images with different filtering methods and (ii) extracting the lumen by an object definition method. The combination of different filtering methods and object definition methods results in a total of 21 methods and 80 experiments. The results are compared with a reference image, obtained from manual editing, by use of four different quality parameters—two based on squared distances and two based on Mahalanobis distances. The evaluation has been carried out on 15 images, of which seven are obtained before balloon dilation and eight after balloon dilation. While for the post-dilation images no definite conclusions can be drawn, an automated contour model applied to images smoothed with a large kernel appears to be a good alternative to manual contouring. For pre-dilation images a fully automated active contour model, initialized by thresholding, preceded by filtering with a small-scale median filter is the best alternative for manual delineation. The results of this method are even better than manual segmentation, i.e. they are consistently closer to the reference image than the average distance of all individual manual segmentations. <s> BIB020 </s> Ultrasound image segmentation: a survey <s> C. Priors 1) Image Features: <s> Some computer applications for tissue characterization in medicine and biology, such as analysis of the myocardium or cancer recognition, operate with tissue samples taken from very small areas of interest. In order to perform texture characterization in such an application, only a few texture operators can be employed: the operators should be insensitive to noise and image distortion and yet be reliable in order to estimate texture quality from the small number of image points available. In order to describe the quality of infarcted myocardial tissue, the authors propose a new wavelet-based approach for analysis and classification of texture samples with small dimensions. The main idea of this method is to decompose the given image with a filter bank derived from an orthonormal wavelet basis and to form an image approximation with higher resolution. Texture energy measures calculated at each output of the filter bank as well as energies of synthesized images are used as texture features in a classification procedure. The authors propose an unsupervised classification technique based on a modified statistical t-test. The method is tested with clinical data, and the classification results obtained are very promising. The performance of the new method is compared with the performance of several other transform-based methods. The new algorithm has advantages in classification of small and noisy input samples, and it represents a step toward structural analysis of weak textures. <s> BIB021 </s> Ultrasound image segmentation: a survey <s> C. Priors 1) Image Features: <s> Abstract The goal of this study was to investigate the potentials of cooccurrence matrix analysis for the characterization of echographic image texture. Echographic data were obtained by one-dimensional simulations. Various data sets were generated with different number densities of the randomly distributed scatterers and with different levels of structural scattering strength. Cooccurrence matrix parameters estimated for analysis were: angular second moment, contrast, correlation, entropy and kappa. The cooccurrence matrix analysis was tuned by varying its parameters: spatial displacement of the pixel pairs, number of gray levels and size of the window. The parameters reached a saturation level at a number density of 3–5 scatterers per resolution cell (−6 dB width). Similarly, when increasing the displacement, a limit value was reached at 4–20 samples, depending on the size of the resolution cell. Using the Mahalanobis distance as a measure of differentiating between two textures, a systematic inverse relation was observed between the size of the window and the number of gray levels used in the estimation of the cooccurrence matrix. The optimal parameters to differentiate textures without structure appeared to be entropy and angular second moment. A window size of 90 speckle cells and 64 gray levels is needed for this purpose. The effective speckle size was estimated from the mean number of maxima of the demodulated echo signals. Resolved structure results in a periodicity of parameter values with displacement. The periodicity can be calculated by changing the displacement d . Optimal parameters for detecting periodicity are contrast and correlation. Analysis of the correlation between parameters showed that entropy vs. angular second moment and contrast vs. correlation are highly correlated. <s> BIB022 </s> Ultrasound image segmentation: a survey <s> C. Priors 1) Image Features: <s> The shape variation displayed by a class of objects can be represented as probability density function, allowing us to determine plausible and implausible examples of the class. Given a training set of example shapes we can align them into a common co-ordinate frame and use kernel-based density estimation techniques to represent this distribution. Such an estimate is complex and expensive, so we generate a simpler approximation using a mixture of gaussians. We show how to calculate the distribution, and how it can be used in image search to locate examples of the modelled object in new images. <s> BIB023 </s> Ultrasound image segmentation: a survey <s> C. Priors 1) Image Features: <s> The application of a knowledge-based segmentation method to the problem of automatically detecting the outer follicle wall boundary in ultrasonographic images of ovaries is presented. A combination of computer detection and interactive adjustment was used to define an approximate inner follicle-wall boundary, which was then used by the computer algorithm as a priori knowledge to automatically find the outer follicle-wall border. The segmentation algorithm was tested on ultrasonographic images of women's ovaries that were imaged in vivo. The semiautomatic segmentations were compared to segmentations by an expert human observer in terms of border placement differences and in terms of quantitative parameters relevant to the physiologic status of the follicles. These physiological parameters include total and specific signal intensity from the follicle and from the follicle wall. The computer-detected outer follicle wall boundaries correlated well with the human observer-defined wall boundaries, in terms of enclosed follicle area, specific and total follicle signal, enclosed wall area, and specific and total wall signal. The actual border placement differences were also small, with a maximum placement difference of 1.47 +/- 0.83 mm and a root mean square (r.m.s.) placement difference of 0.59 +/- 0.28 mm. <s> BIB024 </s> Ultrasound image segmentation: a survey <s> C. Priors 1) Image Features: <s> Ovarian ultrasound is an effective tool in infertility treatment. Repeated measurements of the size and shape of follicles over several days are the primary means of evaluation by physicians. Currently, follicle wall segmentation is achieved by manual tracing which is time consuming and susceptible to inter-operator variation. An automated method for follicle wall segmentation is reported that uses a four-step process based on watershed segmentation and knowledge-based graph search algorithm which utilizes priori information about follicle structure for inner and outer wall detection. The automated technique was tested on 36 ultrasonographic images of women's ovaries. Validation against manually traced borders has shown good correlation of manually defined and computer-determined area measurements (R2 = 0.85 - 0.96). The border positioning errors were small: 0.63+/-0.36 mm for inner border and 0.67+/-0.41 mm for outer border detection. The use of watershed segmentation and graph search methods facilitates fast, accurate inner and outer border detection with minimal user-interaction. <s> BIB025 </s> Ultrasound image segmentation: a survey <s> C. Priors 1) Image Features: <s> This paper presents a method for segmentation and tracking of cardiac structures in ultrasound image sequences. The developed algorithm is based on the active contour framework. This approach requires initial placement of the contour close to the desired position in the image, usually an object outline. Best contour shape and position are then calculated, assuming that at this configuration a global energy function, associated with a contour, attains its minimum. Active contours can be used for tracking by selecting a solution from a previous frame as an initial position in a present frame. Such an approach, however, fails for large displacements of the object of interest. This paper presents a technique that incorporates the information on pixel velocities (optical flow) into the estimate of initial contour to enable tracking of fast-moving objects. The algorithm was tested on several ultrasound image sequences, each covering one complete cardiac cycle. The contour successfully tracked boundaries of mitral valve leaflets, aortic root and endocardial borders of the left ventricle. The algorithm-generated outlines were compared against manual tracings by expert physicians. The automated method resulted in contours that were within the boundaries of intraobserver variability. <s> BIB026 </s> Ultrasound image segmentation: a survey <s> C. Priors 1) Image Features: <s> This paper describes a new approach for texture characterization, based on nonseparable wavelet decomposition, and its application for the discrimination of visually similar diffuse diseases of liver. The proposed feature-extraction algorithm applies nonseparable quincunx wavelet transform and uses energies of the transformed regions to characterize textures. Classification experiments on a set of three different tissue types show that the scale/frequency approach, particularly one based on the nonseparable wavelet transform, could be a reliable method for a texture characterization and analysis of B-scan liver images. Comparison between the quincunx and the traditional wavelet decomposition suggests that the quincunx transform is more appropriate for characterization of noisy data, and practical applications, requiring description with lower rotational sensitivity. <s> BIB027 </s> Ultrasound image segmentation: a survey <s> C. Priors 1) Image Features: <s> This paper presents a quantitative comparison of three types of information available for 52 patients scheduled for carotid endarterectomy: subjective classification of the ultrasound images obtained during scanning before operation, first-and second-order statistical features extracted from regions of the plaque in still ultrasound images from three orthogonal scan planes and finally a histological analysis of the surgically removed plaque. The quantitative comparison was made with the linear model and with separation of the available data into training and test sets. The comparison of subjective classification with features from still ultrasound images revealed an overall agreement of 60% for classification of echogenicity and 70% for classification of structure. Comparison of the histologically determined relative volume of soft materials with features from the still images revealed a correlation coefficient of r = -0.42(p = 0.002). for mean echogenicity of the plaque region. The best performing feature was of second order and denoted Contrast (r = -0.5). Though significant, the latter correlation is probably not strong enough to be useful for clinical prediction of relative volume of soft materials for individual patients. Reasons for this is discussed in the paper, together with suggestions for improvements. <s> BIB028 </s> Ultrasound image segmentation: a survey <s> C. Priors 1) Image Features: <s> Intravascular ultrasound (IVUS) imaging permits direct visualization of vascular pathology. It has been used to evaluate lumen and plaque in coronary arteries and its clinical significance for guidance of coronary interventions is increasingly recognized. Conventional manual evaluation is tedious and time consuming. This paper describes a highly automated approach to segmentation of coronary wall and plaque, and determination of plaque composition in individual IVUS images and pullback image sequences. The determined regions of plaque were classified in one of three classes: soft plaque, hard plaque, or hard plaque shadow. The method's performance was assessed in vitro and in vivo in comparison with observer-defined independent standards. In the analyzed images and image sequences, the mean border positioning error of the wall and plaque borders ranged from 0.13-0.17 mm. Plaque classification correctness was 90%,. <s> BIB029 </s> Ultrasound image segmentation: a survey <s> C. Priors 1) Image Features: <s> OBJECTIVE ::: A specific algorithm is presented for the automatic extraction of breast tumors in ultrasonic imaging. ::: ::: ::: METHOD ::: The algorithm involves two-dimensional adaptive K-means clustering of the gray scale and textural feature images. The segmentation problem is formulated as a maximum a posteriori (MAP) estimation problem. The MAP estimation is achieved using Besag's iterated conditional modes algorithm for the minimization of an energy function. This function has three components: the first constrains the region to be close to the data; the second imposes spatial continuity; and the third takes into consideration the texture of the various regions. A multiresolution implementation of the algorithm is performed using a waveless basis. ::: ::: ::: RESULTS ::: Experiments were carried out on synthetic images and on in vivo breast ultrasound images. Various parameters involved in the algorithm are discussed to evaluate the robustness and accuracy of the segmentation method. ::: ::: ::: CONCLUSION ::: Including textural features in the segmentation of ultrasonic data improves the robustness of the algorithm and makes the segmentation result less parameter dependent. <s> BIB030 </s> Ultrasound image segmentation: a survey <s> C. Priors 1) Image Features: <s> We demonstrate a novel method of interpreting images using an Active Appearance Model (AAM). An AAM contains a statistical model of the shape and grey-level appearance of the object of interest which can generalise to almost any valid example. During a training phase we learn the relationship between model parameter displacements and the residual errors induced between a training image and a synthesised model example. To match to an image we measure the current residuals and use the model to predict changes to the current parameters, leading to a better fit. A good overall match is obtained in a few iterations, even from poor starting estimates. We describe the technique in detail and give results of quantitative performance tests. We anticipate that the AAM algorithm will be an important method for locating deformable objects in many applications. <s> BIB031 </s> Ultrasound image segmentation: a survey <s> C. Priors 1) Image Features: <s> Abstract Intravascular ultrasound (IVUS) provides exact anatomy of arteries, allowing accurate quantitative analysis. Automated segmentation of IVUS images is a prerequisite for routine quantitative analyses. We present a new three-dimensional (3D) segmentation technique, called active surface segmentation, which detects luminal and adventitial borders in IVUS pullback examinations of coronary arteries. The technique was validated against expert tracings by computing correlation coefficients (range 0.83–0.97) and William's index values (range 0.37–0.66). The technique was statistically accurate, robust to image artifacts, and capable of segmenting a large number of images rapidly. Active surface segmentation enabled geometrically accurate 3D reconstruction and visualization of coronary arteries and volumetric measurements. <s> BIB032 </s> Ultrasound image segmentation: a survey <s> C. Priors 1) Image Features: <s> Knowledge about the status of the female reproductive system is important for fertility problems and age-related family planning. The volume of these fertility requests in our emancipated society is steadily increasing. Intravaginal 3D ultrasound imaging of the follicles in the ovary gives important information about the ovarian aging, i.e. number of follicles, size, position and response to hormonal stimulation. Manual analysis of the many follicles is laborious and error-prone. We present a multiscale analysis to automatically detect and quantify the number and shape of the patient's follicles. Robust estimation of the centres of the follicles in the speckled echographic images is done by calculating so-called winding number of the intensity singularity, i.e. the path integral of the angular increment of the direction of the gradient vector over a closed neighbourhood around the point. The principal edges on 200-500 intensity traces radiating from the detected singularity points are calculated by a multiscale edge focussing technique on 1D winding numbers. They are fitted with 3D spherical harmonic functions, from which the volume and shape parameters are derived. <s> BIB033 </s> Ultrasound image segmentation: a survey <s> C. Priors 1) Image Features: <s> Abstract This article describes a novel approach to the semi-automatic segmentation of ultrasound images. Assisted segmentation is particularly attractive when processing many slices through a 3D data set, and even though fully automatic segmentation would be ideal, this is currently not feasible given the quality of ultrasound images. The algorithm developed in this article is based on the active contour paradigm, with several important modifications. The contour is attracted to boundaries described locally by statistical models: this allows for the fact that the definition of what constitutes a boundary may vary around the boundary’s length. The statistical models are trained on-the-fly by observing boundaries accepted by the operator. In this way, operator intervention in a particular slice is sensibly exploited to reduce the need for intervention in subsequent slices. The resulting algorithm provides fast, reliable and verifiable segmentation of in vivo ultrasound images. <s> BIB034 </s> Ultrasound image segmentation: a survey <s> C. Priors 1) Image Features: <s> This study investigated the use of artificial neural networks (ANN) for image segmentation and spatial temporal contour linking for the detection of endocardial contours on echocardiographic images. Using a backpropagation network, the system was trained with 279 sample regions obtained from eight training images to segment images into either tissue or blood pool region. The ANN system was then applied to parasternal short axis images of 38 patients. Spatial temporal contour linking was performed on the segmented images to extract endocardial boarders. Left ventricular areas (end-systolic and end-diastolic) determined with the automated system were calculated and compared to results obtained by manual contour tracing performed by two independent investigators. In addition, ejection fractions (EF) were derived using the area-length method and compared with radionuclide ventriculography. Image quality was classified as good in 12 (32%), moderate in 13 (34%) and poor in 13 (34%) patients. The ANN system provided estimates of end-diastolic and end-systolic areas in 36 (89%) of echocardiograms, which correlated well with those obtained by manual tracing (R = 0.99, SEE = 1.44). A good agreement was also found for the comparison of EF between the ANN system and Tc-radionuclide ventriculography (RNV, R = 0.93, SEE = 6.36). The ANN system also performed well in the subset of patients with poor image quality. Endocardial contour detection using artificial neural networks and spatial temporal contour linking allows accurate calculations of ventricular areas from transthoracic echocardiograms and performs well even in images with poor quality. This system could greatly enhance the feasibility, accuracy and reproducibility of calculating cardiac areas to derive left ventricular volumes and ejection fractions. <s> BIB035 </s> Ultrasound image segmentation: a survey <s> C. Priors 1) Image Features: <s> Introduces a new model for multiscale analysis of space-time echocardiographic sequences. The proposed nonlinear partial differential equation, representing the multiscale analysis, filters the sequence while keeping the space-time coherent structures. It combines the ideas of regularized Perona-Malik anisotropic diffusion and the Galilean invariant movie multiscale analysis of Alvarez et al. (Arch. Rat. Mech. Anal., vol. 123, p. 200-57, 1993). A numerical method for solving the proposed partial differential equation is suggested and its stability is shown. Computational results on synthesized and real sequences are provided. A qualitative and quantitative evaluation of the accuracy of the method is presented. <s> BIB036 </s> Ultrasound image segmentation: a survey <s> C. Priors 1) Image Features: <s> This paper describes a new fully automatic fuzzy multiresolution-based algorithm for cardiac left ventricular (LV) epicardial and endocardial boundary detection and tracking on a sequence of short axis (SA) echocardiographic images of a complete cardiac cycle. This is a necessary step for automatic quantification of cardiac function using echo images, The proposed method is a "center-based" approach in which epicardial and endocardial boundary edge points are searched for on radial lines emanating from the LV center point. The central point of the LV cavity is estimated using a fuzzy-based technique in which the "uncertain" spatial, morphological, and intensity information of the image are represented as fuzzy sets and then combined by fuzzy operators. Edge-detection stage uses multiscale spatial and temporal information in a fuzzy multiresolution framework to identify a single moving edge point for each one of the epicardial and endocardial boundaries over the M radii in the N frames of a complete cardiac cycle. The raw extracted edge points are then processed in the wavelet domain to reduce the effects of noise from the boundaries and papillary muscles from the endocardial boundary extraction process. Finally, a uniform cubic B-spline approximation method is used to define the closed LV boundaries. Experiments with simulated and real echocardiographic images are presented. <s> BIB037 </s> Ultrasound image segmentation: a survey <s> C. Priors 1) Image Features: <s> In this paper a temporal learning-filtering procedure is applied to refine the left ventricle (LV) boundary detected by an active-contour model. Instead of making prior assumptions about the LV shape or its motion, this information is incrementally gathered directly from the images and is exploited to achieve more coherent segmentation. A Hough transform technique is used to find an initial approximation of the object boundary at the first frame of the sequence. Then, an active-contour model is used in a coarse-to-fine framework, for the estimation of a noisy LV boundary. The PCA transform is applied to form a reduced ordered orthonormal basis of the LV deformations based on a sequence of noisy boundary observations. Then this basis Is used to constrain the motion of the active contour in subsequent frames, and thus provide more coherent identification. Results of epicardial boundary identification in B-mode images are presented. <s> BIB038 </s> Ultrasound image segmentation: a survey <s> C. Priors 1) Image Features: <s> This paper describes a new approach to adaptive estimation of parametric deformable contours based on B-spline representations. The problem is formulated in a statistical framework with the likelihood function being derived from a region-based image model. The parameters of the image model, the contour parameters, and the B-spline parameterization order (i.e., the number of control points) are all considered unknown. The parameterization order is estimated via a minimum description length (MDL) type criterion. A deterministic iterative algorithm is developed to implement the derived contour estimation criterion, the result is an unsupervised parametric deformable contour: it adapts its degree of smoothness/complexity (number of control points) and it also estimates the observation (image) model parameters. The experiments reported in the paper, performed on synthetic and real (medical) images, confirm the adequate and good performance of the approach. <s> BIB039 </s> Ultrasound image segmentation: a survey <s> C. Priors 1) Image Features: <s> Abstract Segmentation of intravascular ultrasound images provides important information about the degree of vessel obstruction as well as about the shape and size of plaques. To address the problems of inter- and intra-observer variances associated with conventional manual tracing, a fully automated segmentation was developed. The algorithm is based on the optimisation of a maximum a posteriori estimator, implementing the Rayleigh distribution of speckle and a priori information about the contours. Within 3D image sets, additional information by the blood flow resulting in a decorrelation of the pixels within the luminal boundary is used to initialise the segmentation. To accelerate the estimation, dynamic programming was used. The segmentation algorithm was realised as a Windows® 95 application on a Pentium® II/233 MHz and delivered reliable and reproducible results independent of the catheter position and the total image brightness (except overflow). In contrast, contours drawn by two physicians for an evaluation of 29 clinical cases showed large intra- and inter-observer variances. In vivo images were acquired with a 20 MHz transducer array (EndoSonics InVision TM ). Comparison with the contours drawn by the physicians and histology demonstrates the potential of the segmentation algorithm. <s> BIB040 </s> Ultrasound image segmentation: a survey <s> C. Priors 1) Image Features: <s> We present a new multiscale approach for deformable contour optimization. The method relies on a multigrid minimization method and a coarse-to-fine relaxation algorithm. This approach consists in minimizing a cascade of optimization problems of reduced and increasing complexity instead of considering the minimization problem on the full and original configuration space. Contrary to classical multiresolution algorithms, no reduction of image is applied. The family of defined energy functions are derived from the original (full resolution) objective function, ensuring that the same function is handled at each scale and that the energy decreases at each step of the deformable contour minimization process. The efficiency and the speed of this multiscale optimization strategy is demonstrated in the difficult context of the minimization of a region-based contour energy function ensuring the boundary detection of anatomical structures in ultrasound medical imagery. In this context, the proposed multiscale segmentation method is compared to other classical region-based segmentation approaches such as Maximum Likelihood or Markov Random Field-based segmentation techniques. We also extend this multiscale segmentation strategy to active contour models using a classical edge-based likelihood approach. Finally, time and performance analysis of this approach, compared to the (commonly used) dynamic programming-based optimization procedure, is given and allows to attest the accuracy and the speed of the proposed method. <s> BIB041 </s> Ultrasound image segmentation: a survey <s> C. Priors 1) Image Features: <s> This paper is concerned with the use of the level set formalism to segment anatomical structures in 3D medical images (ultrasound or magnetic resonance images). A closed 3D surface propagates towards the desired boundaries through the iterative evolution of a 4D implicit function. The major contribution of this work is the design of a robust evolution model based on adaptive parameters depending on the data. First the iteration step and the external propagation force, both usually constant, are automatically computed at each iteration. Additionally, region-based information rather than the gradient is used, via an estimation of intensity probability density functions over the image. As a result, the method can be applied to various kinds of data. Quantitative and qualitative results on brain MR images and 3D echographies of carotid arteries are discussed. <s> BIB042 </s> Ultrasound image segmentation: a survey <s> C. Priors 1) Image Features: <s> Accurate detection of prostate boundaries is required in many diagnostic and treatment procedures for prostate disease. In this paper, a new paradigm for guided edge delineation is described, which involves presenting automatically detected prostate edges as a visual guide to the observer, followed by manual editing. This approach enables robust delineation of the prostate boundaries, making it suitable for routine clinical use. The edge-detection algorithm is comprised of three stages. An algorithm called sticks is used to enhance contrast and at the same time reduce speckle in the transrectal ultrasound prostate image. The resulting image is further smoothed using an anisotropic diffusion filter. In the third stage, some basic prior knowledge of the prostate, such as shape and echo pattern, is used to detect the most probable edges describing the prostate. Finally, patient-specific anatomic information is integrated during manual linking of the detected edges. The algorithm was tested on 125 images from 16 patients. The performance of the algorithm was statistically evaluated by employing five expert observers. Based on this study, we found that consistency in prostate delineation increases when automatically detected edges are used as visual guide during outlining, while the accuracy of the detected edges was found to be at least as good as those of the human observers. The use of edge guidance for boundary delineation can also be extended to other applications in medical imaging where poor contrast in the images and the complexity in the anatomy limit the clinical usability of fully automatic edge-detection techniques. <s> BIB043 </s> Ultrasound image segmentation: a survey <s> C. Priors 1) Image Features: <s> Automated edge detection may standardize measurements among observers, providing for rapid assessment of intravascular ultrasound (IVUS) images. However, with high frequency images, enhanced blood signals make it difficult to define and trace the lumen borders. Accordingly, we evaluated a fully automated contour analysis facilitated with a blood noise reduction algorithm (BNR) for 40-MHz IVUS images in human coronary arteries of 27 patients. This algorithm is based on the principle that blood echo speckles have higher temporal and spatial variations than the arterial wall. A total of 193 paired lumen areas and 78 external elastic membrane (EEM) areas were measured and compared. Automated measurements showed good agreement with manual tracings for lumen and EEM area, with high correlation coefficients (0.945 and 0.950, respectively) and small variability (0.4 ± 14.4% and 0.6 ± 9.7%, respectively). This preliminary finding suggests that automated contour detection facilitated with BNR appeared to be a feasible and reliable technique for area measurements in 40-MHz IVUS imaging. <s> BIB044 </s> Ultrasound image segmentation: a survey <s> C. Priors 1) Image Features: <s> Intravascular ultrasound (IVUS) provides direct depiction of coronary artery anatomy, including plaque and vessel area, which is important in quantitative studies on the progression or regression of coronary artery disease. Traditionally, these studies have relied on manual evaluation, which is laborious, time consuming, and subject to large interobserver and intraobserver variability. A new technique, called active surface segmentation, alleviates these limitations and makes strides toward routine analyses. However, for three-dimensional (3-D) plaque assessment or 3-D reconstruction to become a clinical reality, methods must be developed which can analyze many images quickly. Presented is a comparison between two active surface techniques for three-dimensional segmentation of luminal and medial-adventitial borders. The force-acceleration technique and the neighborhood-search technique accurately detected both borders in vivo (r2 = 0.95 and 0.99, Williams' index = 0.67 and 0.65, and r2 = 0.95 and 0.99, WI = 0.67 and 0.70, respectively). However, the neighborhood-search technique was significantly faster and required less computation. Volume calculations for both techniques (r2 = 0.99 and r2 = 0.99) also agreed with a known-volume phantom. Active surface segmentation allows 3-D assessment of coronary morphology and further developments with this technology will provide clinical analysis tools. <s> BIB045 </s> Ultrasound image segmentation: a survey <s> C. Priors 1) Image Features: <s> We present a model and algorithm for segmentation of images with missing boundaries. In many situations, the human visual system fills in missing gaps in edges and boundaries, building and completing information that is not present. This presents a considerable challenge in computer vision, since most algorithms attempt to exploit existing data. Completion models, which postulate how to construct missing data, are popular but are often trained and specific to particular images. In this paper, we take the following perspective: We consider a reference point within an image as given and then develop an algorithm that tries to build missing information on the basis of the given point of view and the available information as boundary data to the algorithm. We test the algorithm on some standard images, including the classical triangle of Kanizsa and low signal/noise ratio medical images. <s> BIB046 </s> Ultrasound image segmentation: a survey <s> C. Priors 1) Image Features: <s> Abstract In this paper we address the problem of spatio-temporal acoustic boundary detection in echocardiography. We propose a phase-based feature detection method to be used as the front end to higher-level 2D+T/3D+T reconstruction algorithms. We develop a 2D+T version of this algorithm and illustrate its performance on some typical echocardiogram sequences. We show how our temporal-based algorithm helps to reduce the number of spurious feature responses due to speckle and provides feature velocity estimates. Further, our approach is intensity-amplitude invariant. This makes it particularly attractive for echocardiographic segmentation, where choosing a single global intensity-based edge threshold is problematic. <s> BIB047 </s> Ultrasound image segmentation: a survey <s> C. Priors 1) Image Features: <s> Intravascular Ultrasound (IVUS) is a diagnostic imaging technique that provides tomographic visualization of coronary arteries. The aim of this study was to evaluate five texture analysis techniques and determine their ability to distinguish between plaque lesions of different composition. Using histological correlation, regions of calcified, fibrous, and necrotic core plaque were chosen from 27 coronary plaques. First-order statistics, Haralick's method, Laws' texture energy method, the neighborhood gray-tone difference matrix method, and texture spectrum features were examined using discriminant analysis. Self-validation indicated that Haralick's method yielded the most accurate results, with resubstitution and cross-validation error rates of 0.00 and 14.76%, respectively. Further optimization gave error rates of 6.67%, using only two discriminating features, IDM and entropy. <s> BIB048 </s> Ultrasound image segmentation: a survey <s> C. Priors 1) Image Features: <s> Echocardiographic quantitation of myocardial texture for diagnosis of early cardiomyopathy (CMP) remains problematic. Conventional statistical methods are limited, contributed by a small image region-of-interest (ROI) and difficulty in discrimination from noise. This study was performed to evaluate the 2-D Haar wavelet decomposition method as a tool to identify textural changes in a rat model of early CMP, focusing on changes that occur before development of M-mode structural abnormalities. Early diabetic CMP, ethanol CMP and diabetic-ethanol CMP rat models were evaluated. Echocardiography was performed on two groups of rats. Group I (5 week cohort, n = 4 per subgroup) included controls, rats on 12% ethanol, a diabetic subgroup, and diabetic rats on 4% ethanol. Group II (10 week cohort, n = 5 per subgroup) included the same categories as group I with an additional subgroup taking 4% ethanol was also studied. M-mode left ventricular measurements were comparable in all subgroups of group I. However, diabetic rats in group II had an increased left ventricular dimension (LVD) compared to all others and an increased septal dimension (IVSD) and posterior wall dimension (PWD) were noted in the 4% and 12% ethanol groups. End-diastolic digital images of all rats in the parasternal short axis view, at the papillary muscle level, were downloaded to a computer. A 16 x 16 (ROI) was selected from the anterior interventricular septum. Although standard statistical methods could not differentiate any of the groups, calculation of textural energy and normalized textural energy with the 2-D Haar wavelet decomposition method found at 5 weeks increased normalized texture energy in diabetics compared to all others. At 10 weeks increased texture energy was noted in diabetics. Diabetic-ethanol rats at both 5 and 10 weeks revealed a blunted textural energy compared to diabetic rats. In a rat model of diabetic cardiomyopathy, the 2-D wavelet decomposition method identified textural energy changes before development of echocardiographic structural changes. Ethanol-associated blunting of textural changes in diabetic rats was also noted. This method for quantitation of ventricular texture may be relevant for diagnosis of early cardiomyopathy. <s> BIB049 </s> Ultrasound image segmentation: a survey <s> C. Priors 1) Image Features: <s> We present a new approach to shape-based segmentation and tracking of deformable anatomical structures in medical images, and validate this approach by detecting and tracking the endocardial contour in an echocardiographic image sequence. To this end, some global prior shape knowledge of the endocardial boundary is captured by a prototype template with a set of predefined global and local deformations to take into account its inherent natural variability over time. In this deformable model-based Bayesian segmentation, the data likelihood model relies on an accurate statistical modelling of the grey level distribution of each class present in the ultrasound image. The parameters of this distribution mixture are given by a preliminary iterative estimation step. This estimation scheme relies on a Markov Random Field prior model, and takes into account the imaging process as well as the distribution shape of each class present in the image. Then the detection and the tracking problem is stated in a Bayesian framework, where it ends up as a cost function minimisation problem for each image of the sequence. In our application, this energy optimisation problem is efficiently solved by a genetic algorithm combined with a steepest ascent procedure. This technique has been successfully applied on synthetic images, and on a real echocardiographic image sequence. <s> BIB050 </s> Ultrasound image segmentation: a survey <s> C. Priors 1) Image Features: <s> An algorithm devoted to the segmentation of 3-D ultrasonic data is proposed. The algorithm involves 3-D adaptive clustering based on multiparametric information: the gray-scale intensity of the echographic data, 3-D texture features calculated from the envelope data, and 3-D tissue characterization information calculated from the local frequency spectra of the radio-frequency signals. The segmentation problem is formulated as a Maximum A posterior (MAP) estimation problem. A multi-resolution implementation of the algorithm is proposed. The approach is tested on simulated data and on in vivo echocardiographic 3-D data. The results presented in the paper illustrate the robustness and the accuracy of the proposed approach for the segmentation of ultrasonic data. <s> BIB051 </s> Ultrasound image segmentation: a survey <s> C. Priors 1) Image Features: <s> This paper presents a method of four-dimensional (4-D) (3-D+Time) space-frequency analysis for directional denoising and enhancement of real-time three-dimensional (RT3D) ultrasound and quantitative measures in diagnostic cardiac ultrasound. Expansion of echocardiographic volumes is performed with complex exponential wavelet-like basis functions called brushlets. These functions offer good localization in time and frequency and decompose a signal into distinct patterns of oriented harmonics, which are invariant to intensity and contrast range. Deformable-model segmentation is carried out on denoised data after thresholding of transform coefficients. This process attenuates speckle noise while preserving cardiac structure location. The superiority of 4-D over 3-D analysis for decorrelating additive white noise and multiplicative speckle noise on a 4-D phantom volume expanding in time is demonstrated. Quantitative validation, computed for contours and volumes, is performed on in vitro balloon phantoms. Clinical applications of this spatiotemporal analysis tool are reported for six patient cases providing measures of left ventricular volumes and ejection fraction. <s> BIB052 </s> Ultrasound image segmentation: a survey <s> C. Priors 1) Image Features: <s> Abstract Most deformable models require the initial contour to be placed close to the boundary of the object of interest for boundary extraction of ultrasound (US) images, which is impractical in many clinical applications. To allow a distant initial contour, a new dual-snake model promising high penetrability through the interference of the noises is proposed in this paper. The proposed dual-snake model features a new far-reaching external force, called the discrete gradient flow, a connected component-weighted image force, and an effective stability evaluation of two underlying snakes. The experimental results show that, with a distant initial contour, the mean distance from the derived boundary to the desired boundary is less than 1.4 pixels, and most snake elements are within 2.7 pixels of the desired boundaries for the synthetic images with CNR ≥ 1. For the clinical US images, the mean distance is less than 1.9 pixels, and most snake elements are within 3 pixels of the desired boundaries. (E-mail: chung@lotus.mc.ntu.edu.tw) <s> BIB053 </s> Ultrasound image segmentation: a survey <s> C. Priors 1) Image Features: <s> An aneurysm of the abdominal aorta (AAA) is characterized by modified wall properties, and a balloon-like area usually filled by a thrombus. A rupture of an aortic aneurysm can be fatal, yet there is no way to accurately predict such an occurrence. The study of the wall and thrombus cross-sectional distension, due to a pressure wave, is important as a way of assessing the degradation of the mechanical properties of the vessel wall and the risk of a rupture. Echo ultrasound transverse cross-sectional imaging is used here to study the thrombus and the aortic wall distension, requiring their segmentation within the image. Polar coordinates are defined, and a search is performed for minimizing a cost function, which includes a description of the boundary (based on a limited series of sine and cosine functions) and information from the image intensity gradients along the radii. The method is based on filtering by a modified Canny-Deriche edge detector and then on minimization of an energy function based on five parts. Since echoes from blood in the lumen and the thrombus produce similar patterns and speckle noise, a modified version for identifying the lumen-thrombus border was developed. The method has been validated by various ways, including parameter sensitivity testing and comparison to the performance of an expert. It is robust enough to track the lumen and total arterial cross-sectional area changes during the cardiac cycle. In 34 patients where sequences of images were acquired, the border between the thrombus and the arterial wall was detected with errors less than 2%, while the lumen-thrombus border was detected with a mean error of 4%. Thus, a noninvasive measurement of the AAA cross-sectional area is presented, which has been validated and found to be accurate. <s> BIB054 </s> Ultrasound image segmentation: a survey <s> C. Priors 1) Image Features: <s> This paper proposes a new fractal feature vector based on M-band wavelet transform for classification of ultrasonic liver images-normal liver, cirrhosis, and hepatoma. Classifications for liver images have revealed that the fractal feature vector is trustworthy. A hierarchical classifier produces 97.27% correct classification for the distinction of normal and abnormal liver image and 93.6% correct classification for the distinction of cirrhosis and hepatoma liver image. <s> BIB055 </s> Ultrasound image segmentation: a survey <s> C. Priors 1) Image Features: <s> In this paper we present a computationally efficient segmentation algorithm for breast masses on sonography that is based on maximizing a utility function over partition margins defined through gray-value thresholding of a preprocessed image. The performance of the segmentation algorithm is evaluated on a database of 400 cases in two ways. Of the 400 cases, 124 were complex cysts, 182 were benign solid lesions, and 94 were malignant lesions. In the first evaluation, the computer-delineated margins were compared to manually delineated margins. At an overlap threshold of 0.40, the segmentation algorithm correctly delineated 94% of the lesions. In the second evaluation, the performance of our computer-aided diagnosis method on the computer-delineated margins was compared to the performance of our method on the manually delineated margins. Round robin evaluation yielded Az values of 0.90 and 0.87 on the manually delineated margins and the computer-delineated margins, respectively, in the task of distinguishing between malignant and nonmalignant lesions. <s> BIB056 </s> Ultrasound image segmentation: a survey <s> C. Priors 1) Image Features: <s> The current practice in assessing sonographic findings of chronic inflamed thyroid tissue is mainly qualitative, based just on a physician's experience. This study shows that inflamed and healthy tissues can be differentiated by automatic texture analysis of B-mode sonographic images. Feature selection is the most important part of this procedure. We employed two selection schemes for finding recognition-optimal features: one based on compactness and separability and the other based on classification error. The full feature set included Muzzolini's spatial features and Haralick's co-occurrence features. These features were selected on a set of 2430 sonograms of 81 subjects, and the classifier performance was evaluated on a test set of 540 sonograms of 18 independent subjects. A classification success rate of 100% was achieved with as few as one optimal feature among the 129 texture characteristics tested. Both selection schemes agreed on the best features. The results were confirmed on the independent test set. The stability of the results with respect to sonograph setting, thyroid gland segmentation and scanning direction was tested. <s> BIB057 </s> Ultrasound image segmentation: a survey <s> C. Priors 1) Image Features: <s> Edge detection is an important, but difficult, step in quantitative ultrasound (US) image analysis. In this paper, we present a new textural approach for detecting a class of edges in US images; namely, the texture edges with a weak regional mean gray-level difference (RMGD) between adjacent regions. The proposed approach comprises a vision model-based texture edge detector using Gabor functions and a new texture-enhancement scheme. The experimental results on the synthetic edge images have shown that the performances of the four tested textural and nontextural edge detectors are about 20%-95% worse than that of the proposed approach. Moreover, the texture enhancement may improve the performance of the proposed texture edge detector by as much as 40%. The experiments on 20 clinical US images have shown that the proposed approach can find reasonable edges for real objects of interest with the performance of 0.4 +/- 0.08 in terms of the Pratt's figure. <s> BIB058 </s> Ultrasound image segmentation: a survey <s> C. Priors 1) Image Features: <s> We developed an adaptive template-matching method for vessel wall boundary detection in brachial artery ultrasound (US) scans. The computer-aided identification of lumen-intima and media-adventitia boundaries of the brachial artery US scan is a critical step for flow-mediated vasodilator response measurement, which has become an increasingly important tool in both clinical and research applications. The adaptive template we propose in this paper is a combination of two parameterized Gaussian envelopes that approximates the US pulse envelope. The algorithm for template matching is cast as a constrained nonlinear least squares problem. A best match between the template and the observed US scan line signal results in a set of optimum parameters of the adaptive template, and the location of the boundaries is directly computed from these parameters. We present the results of applying our method to synthetic and clinical data, showing that the adaptive template-matching method provides superior accuracy in detecting the media-adventitia boundaries of both the near wall and the far wall of the vessel to that of conventional edge-detection methods. <s> BIB059 </s> Ultrasound image segmentation: a survey <s> C. Priors 1) Image Features: <s> Abstract This paper presents a new technique for semiautomatic quantification of regional heart function from 2-D echocardiography. It uses a novel left ventricular border tracking algorithm based on shape-space ideas that we have recently described. In this paper, we show how to decompose the tracked output into clinically meaningful segmental parameters (wall excursion and thickening), using what we call a computational interpretational space (CIS). This leads to a quantitative and automatic scoring scheme for endocardial excursion and myocardial thickening. The method is illustrated on data from a patient with a myocardial infarct in the apical anterior/inferior region of the heart and is also assessed in a small retrospective dobutamine stress echocardiography clinical case study. (E-mail: noble@robots.ox.ac.uk) <s> BIB060 </s> Ultrasound image segmentation: a survey <s> C. Priors 1) Image Features: <s> Deformable template models and Markov chain Monte Carlo methods are used for analysing a space-time process of intracoronary ultrasound images in order to detect the artery contour and various other characteristics as a function of time. <s> BIB061 </s> Ultrasound image segmentation: a survey <s> C. Priors 1) Image Features: <s> In the automatic segmentation of echocardiographic images, a priori shape knowledge is used to compensate poor features in ultrasound images. The shape knowledge is often learned via off-line training process, which requires tedious human effort and is unavoidably expertise-dependent. More importantly, a learned shape template can only be used to segment a specific class of images with similar boundary shapes.In this paper, we present a multi-scale level set framework for echo image segmentation. We extract echo image boundaries automatically at a very coarse scale. These boundaries are then not only used as boundary initials at finer scales, but also as an external constraint to guide contour evolutions. This constraint functions similar to a traditional shape prior. Experimental results validate this combinative framework. <s> BIB062 </s> Ultrasound image segmentation: a survey <s> C. Priors 1) Image Features: <s> Two common deficiencies of most conventional deformable models are the need to place the initial contour very close to the desired boundary and the incapability of capturing a highly winding boundary for sonographic boundary extraction. To remedy these two deficiencies, a new deformable model (namely, the cell-based dual snake model) is proposed in this paper. The basic idea is to apply the dual snake model in the cell-based deformation manner. While the dual snake model provides an effective mechanism allowing a distant initial contour, the cell-based deformation makes it possible to catch the winding characteristics of the desired boundary. The performance of the proposed cell-based dual snake model has been evaluated on synthetic images with simulated speckles and on the clinical ultrasound (US) images. The experimental results show that the mean distances from the derived to the desired boundary points are 0.9 +/- 0.42 pixels and 1.29 +/- 0.39 pixels for the synthetic and the clinical US images, respectively. <s> BIB063 </s> Ultrasound image segmentation: a survey <s> C. Priors 1) Image Features: <s> Abstract An improved algorithm is presented for ovarian follicle detection in ultrasound images. This fully automated recognition algorithm is composed of three successive steps. First, initial homogeneous regions are determined. Then, these initial regions are grown. The growing is controlled by average grey-level and by a newly introduced weighted image gradient. In the last stage, those regions are extracted that probably correspond to the follicles. The algorithm has been tested on 50 ovarian ultrasound images. The recognition rate of follicles using this procedure was around 78%. A possible extension of the algorithm deals with the entire information in the ultrasound image sequence, which is covered in Part II of this paper. <s> BIB064 </s> Ultrasound image segmentation: a survey <s> C. Priors 1) Image Features: <s> The application of level set techniques to echocardiographic data is presented. This method allows semiautomatic segmentation of heart chambers, which regularizes the shapes and improves edge fidelity, especially in the presence of gaps, as is common in ultrasound data. The task of the study was to reconstruct left ventricular shape and to evaluate left ventricular volume. Data were acquired with a real-time three-dimensional (3-D) echocardiographic system. The method was applied directly in the three-dimensional domain and was based on a geometric-driven scheme. The numerical scheme for solving the proposed partial differential equation is borrowed from numerical methods for conservation law. Results refer to in vitro and human in vivo acquired 3-D+time echocardiographic data. Quantitative validation was performed on in vitro balloon phantoms. Clinical application of this segmentation technique is reported for 20 patient cases providing measures of left ventricular volumes and ejection fraction. <s> BIB065 </s> Ultrasound image segmentation: a survey <s> C. Priors 1) Image Features: <s> A new method is proposed to reconstruct and analyze the left ventricle (LV) from multiple acoustic window three-dimensional (3-D) ultrasound acquired using a transthoracic 3-D rotational probe. Prior research in this area has been based on one acoustic window acquisition. However, the data suffers from several limitations that degrade the reconstruction and reduce the clinical value of interpretation, such as the presence of shadow due to bone (ribs) and air (in the lungs) and motion of the probe during the acquisition. In this paper, we show how to overcome these limitations by automatically fusing information from multiple acoustic window sparse-view acquisitions and using a position sensor to track the probe in real time. Geometric constraints of the object shape, and spatiotemporal information relating to the image acquisition process, are used in new algorithms for 1) grouping endocardial edge cues from an initial image segmentation and 2) defining a novel reconstruction method that utilizes information from multiple acoustic windows. The new method has been validated on a phantom and three real heart data sets. In the phantom study, one finger of a latex glove was scanned from two acoustic windows and reconstructed using the new method. The volume error was measured to be less than 4%. In the clinical case study, 3-D ultrasound and magnetic resonance imaging (MRI) scanning were performed on the same healthy volunteers. Quantitative ejection fractions (EFs) and volume-time curves over a cardiac cycle were estimated using the new method and compared to cardiac MRI measurements. This showed that the new method agrees better with MRI measurements than the previous approach we have developed based on a single acoustic window. The EF errors of the new method with respect to MRI measurements were less than 6%. A more extensive clinical validation is required to establish whether these promising first results translate to a method suitable for routine clinical use. <s> BIB066 </s> Ultrasound image segmentation: a survey <s> C. Priors 1) Image Features: <s> A major barrier for using 3-D echocardiography for quantitative analysis of heart function in routine clinical practice is the absence of accurate and robust segmentation and tracking methods necessary to make the analysis automatic. In this paper, we present an automated three-dimensional (3-D) echocardiographic acquisition and image-processing methodology for assessment of left ventricular (LV) function. We combine global image information provided by a novel multiscale fuzzy-clustering segmentation algorithm, with local boundaries obtained with phase-based acoustic feature detection. We then use the segmentation results to fit and track the LV endocardial surface using a 3-D continuous transformation. To our knowledge, this is the first report of a completely automated method. The protocol is evaluated in a small clinical case study (nine patients). We compare ejection fractions (EFs) computed with the new approach to those obtained using the standard clinical technique, single-photon emission computed tomography multigated acquisition. Errors on six datasets were found to be within six percentage points. A further two, with poor image quality, improved upon EFs from manually delineated contours, and the last failed due to artifacts in the data. Volume-time curves were derived and the results compared to those from manual segmentation. Improvement over an earlier published version of the method is noted. <s> BIB067 </s> Ultrasound image segmentation: a survey <s> C. Priors 1) Image Features: <s> When performing block-matching based motion estimation with the ML estimator, one would try to match blocks from the two images, within a predefined search area. The estimated motion vector is that which maximizes a likelihood function, formulated according to the image formation model. Two new maximum likelihood motion estimation schemes for ultrasound images are presented. The new likelihood functions are based on the assumption that both images are contaminated by a Rayleigh distributed multiplicative noise. The new approach enables motion estimation in cases where a noiseless reference image is not available. Experimental results show a motion estimation improvement with regards to other known ML estimation methods. <s> BIB068 </s> Ultrasound image segmentation: a survey <s> C. Priors 1) Image Features: <s> We present a computer-aided diagnosis(CAD) method for breast lesions on ultrasound that is based on the automatic segmentation of lesions and the automatic extraction of four features related to the lesion shape, margin, texture, and posterior acoustic behavior. Using a database of 400 cases (94 malignant lesions, 124 complex cysts, and 182 benign solid lesions), we investigate the marginal benefit of each feature in our CAD method and the performance of our CAD method in distinguishing malignant lesions from various classes of benign lesions. Finally, independent validation is performed on our CAD method. Eleven independent trials yielded an average A z value of 0.87 in the task of distinguishing malignant from benign lesions. <s> BIB069 </s> Ultrasound image segmentation: a survey <s> C. Priors 1) Image Features: <s> We investigated the use of a radial gradient index (RGI) filtering technique to automatically detect lesions on breast ultrasound. After initial RGI filtering, a sensitivity of 87% at 0.76 false-positive detections per image was obtained on a database of 400 patients (757 images). Next, lesion candidates were segmented from the background by maximizing an average radial gradient (ARD) index for regions grown from the detected points. At an overlap of 0.4 with a radiologist lesion outline, 75% of the lesions were correctly detected. Subsequently, round robin analysis was used to assess the quality of the classification of lesion candidates into actual lesions and false-positives by a Bayesian neural network. The round robin analysis yielded an Az value of 0.84, and an overall performance by case of 94% sensitivity at 0.48 false-positives per image. Use of computerized analysis of breast sonograms may ultimately facilitate the use of sonography in breast cancer screening programs. <s> BIB070 </s> Ultrasound image segmentation: a survey <s> C. Priors 1) Image Features: <s> To increase the ability of ultrasonographic technology for the differential diagnosis of solid breast tumors, we describe a novel computer-aided diagnosis (CADx) system using neural networks for classification of breast tumors. Tumor regions and surrounding tissues are segmented from the physician-located region-of-interest (ROI) images by applying our proposed segmentation algorithm. Cooperating with the segmentation algorithm, three feasible features, including variance contrast, autocorrelation contrast and distribution distortion of wavelet coefficients, were extracted from the ROI images for further classification. A multilayered perceptron (MLP) neural network trained using error back-propagation algorithm with momentum was then used for the differential diagnosis of breast tumors on sonograms. In the experiment, 242 cases (including benign breast tumors from 161 patients and carcinomas from 82 patients) were sampled with k-fold cross-validation (k = 10) to evaluate the performance. The receiver operating characteristic (ROC) area index for the proposed CADx system is 0.9396 +/- 0.0183, the sensitivity is 98.77%, the specificity is 81.37%, the positive predictive value is 72.73% and the negative predictive value is 99.24%. Experimental results showed that our diagnosis model performed very well for breast tumor diagnosis. <s> BIB071 </s> Ultrasound image segmentation: a survey <s> C. Priors 1) Image Features: <s> A hybrid neural network is presented for the segmentation of ultrasound images.Feature vectors are formed by the discrete cosine transform of pixel intensities in region of interest (ROI). The elements and the dimension of the feature vectors are determined by considering only two parameters: The amount of ignored coefficients, and the dimension of the ROI.First-layer-nodes of the proposed hybrid network represent hyperspheres (HSs) in the feature space. Feature space is partitioned by intersecting these HSs to represent the distribution of classes. The locations and radii of the HSs are found by the genetic algorithms.Restricted Coulomb energy (RCE) network, modified RCE network, multi-layer perceptron and the proposed hybrid neural network are examined comparatively for the segmentation of ultrasound images. <s> BIB072 </s> Ultrasound image segmentation: a survey <s> C. Priors 1) Image Features: <s> The purpose of this study was to test the efficacy of using small training sets in computer-aided diagnostic systems (CAD) and to increase the capabilities of ultrasound (US) technology in the differential diagnosis of solid breast tumors. A total of 263 sonographic images of solid breast nodules, including 129 malignancies and 134 benign nodules, were evaluated by using a bootstrap technique with 10 original training samples. Texture parameters of a region-of-interest (ROI) were resampled with a bootstrap technique and a decision-tree model was used to classify the tumor as benign or malignant. The accuracy was 87.07% (229 of 263 tumors), the sensitivity was 95.35% (123 of 129), the specificity was 79.10% (106 of 134), the positive predictive value was 81.46% (123 of 151), and the negative predictive value was 94.64% (106 of 112). This analysis method provides a second opinion for physicians with high accuracy. The new method shows a potential to be useful in future application of CAD, especially when a large database cannot be obtained for training or a newly developed ultrasonic system has smaller sets of samples. <s> BIB073 </s> Ultrasound image segmentation: a survey <s> C. Priors 1) Image Features: <s> A novel extension of active appearance models (AAMs) for automated border detection in echocardiographic image sequences is reported. The active appearance motion model (AAMM) technique allows fully automated robust and time-continuous delineation of left ventricular (LV) endocardial contours over the full heart cycle with good results. Nonlinear intensity normalization was developed and employed to accommodate ultrasound-specific intensity distributions. The method was trained and tested on 16-frame phase-normalized transthoracic four-chamber sequences of 129 unselected infarct patients, split randomly into a training set (n = 65) and a test set (n = 64). Borders were compared to expert drawn endocardial contours. On the test set, fully automated AAMM performed well in 97% of the cases (average distance between manual and automatic landmark points was 3.3 mm, comparable to human interobserver variabilities). The ultrasound-specific intensity normalization proved to be of great value for good results in echocardiograms. The AAMM was significantly more accurate than an equivalent set of two-dimensional AAMs. <s> BIB074 </s> Ultrasound image segmentation: a survey <s> C. Priors 1) Image Features: <s> A model-based method for three-dimensional image segmentation was developed and its performance assessed in segmentation of volumetric cardiac magnetic resonance (MR) images and echocardiographic temporal image sequences. Comprehensive design of a three-dimensional (3-D) active appearance model (AAM) is reported for the first time as an involved extension of the AAM framework introduced by Cootes et al. The model's behavior is learned from manually traced segmentation examples during an automated training stage. Information about shape and image appearance of the cardiac structures is contained in a single model. This ensures a spatially and/or temporally consistent segmentation of three-dimensional cardiac images. The clinical potential of the 3-D AAM is demonstrated in short-axis cardiac MR images and four-chamber echocardiographic sequences. The method's performance was assessed by comparison with manually identified independent standards in 56 clinical MR and 64 clinical echo image sequences. The AAM method showed good agreement with the independent standard using quantitative indexes of border positioning errors, endo- and epicardial volumes, and left ventricular mass. In MR, the endocardial volumes, epicardial volumes, and left ventricular wall mass correlation coefficients between manual and AAM were R/sup 2/=0.94,0.97,0.82, respectively. For echocardiographic analysis, the area correlation was R/sup 2/=0.79. The AAM method shows high promise for successful application to MR and echocardiographic image analysis in a clinical setting. <s> BIB075 </s> Ultrasound image segmentation: a survey <s> C. Priors 1) Image Features: <s> A novel method of incorporating shape information into the image segmentation process is presented. We introduce a representation for deformable shapes and define a probability distribution over the variances of a set of training shapes. The segmentation process embeds an initial curve as the zero level set of a higher dimensional surface, and evolves the surface such that the zero level set converges on the boundary of the object to be segmented. At each step of the surface evolution, we estimate the maximum a posteriori (MAP) position and shape of the object in the image, based on the prior shape information and the image information. We then evolve the surface globally, towards the MAP estimate, and locally, based on image gradients and curvature. Results are demonstrated on synthetic data and medical imagery, in 2D and 3D. <s> BIB076 </s> Ultrasound image segmentation: a survey <s> C. Priors 1) Image Features: <s> This paper presents a new semi-automatic method for quantifying regional heart function from two-dimensional echocardiography. In the approach, we first track the endocardial and epicardial boundaries using a new variant of the dynamic snake approach. The tracked borders are then decomposed into clinically meaningful regional parameters, using a novel interpretational shape-space motivated by the 16-segment model used in clinical practice for qualitative assessment of heart function. We show how a quantitative and automatic scoring scheme for the endocardial excursion and myocardial thickening can be derived from this. Results illustrating our approach on apical long-axis two-chamber-view data from a patient with a myocardial infarct in the apical anterior/inferior region of the heart are presented. In a case study (five patients, nine data sets) the performance of the tracking and interpretation techniques are compared with manual delineations of borders using a number of quantitative measures of regional comparison. <s> BIB077 </s> Ultrasound image segmentation: a survey <s> C. Priors 1) Image Features: <s> The presence of speckle in ultrasound images makes it hard to segment them using active contours. Speckle causes the energy function of the active contours to have many local minima, and the gradient descent procedure used for evolving the contour gets trapped in these minima. <s> BIB078 </s> Ultrasound image segmentation: a survey <s> C. Priors 1) Image Features: <s> Abstract In this paper automatic detection of the boundary of left ventricle (LV) in a sequence of cardiac images has been proposed. The contour detection algorithm is formulated as a constrained optimization problem based on active contour model. The optimization problem has been solved using Genetic Algorithm (GA). The result obtained by the proposed GA based approach is compared with conventional nonlinear programming methods. Validation of the computer-generated boundaries is done after comparing them with manually outlined contours by expert observers. The performance of the algorithm is comparable to inter-observer anomalies. <s> BIB079 </s> Ultrasound image segmentation: a survey <s> C. Priors 1) Image Features: <s> An improved fast marching approach for endocardial boundary detection and tracking in echocardiographic images is presented. Firstly, the traditional fast marching algorithm is applied to the echocardiographic images. And the existing problems are discussed. Then, the algorithm is improved by introducing the average energy of the whole advancing front into the speed term instead of determining the speed term only with the local image features. The experimental results show that the improved algorithm is very effective and stable. <s> BIB080 </s> Ultrasound image segmentation: a survey <s> C. Priors 1) Image Features: <s> In this note we present a coupled optimization model for boundary determination. One part of the model incorporates a prior shape into a geometric active contour model with a fixed parameter. The second part determines the 'best' parameter used in the first part by maximizing the mutual information of the image geometry between the prior and an aligned novel image over all the alignments that are the solutions of the first part corresponding to different parameters. We also present an alternative method, which generates an intensity model formed as the average of a set of aligned training images. Experimental results on cardiac ultrasound images are presented. These results indicate that the proposed model provides close agreement with expert traced borders, and the parameter determined in this model for one image can be used for images with similar properties. The existence of a solution to the proposed minimization problem is also discussed. <s> BIB081 </s> Ultrasound image segmentation: a survey <s> C. Priors 1) Image Features: <s> In this paper, we apply the three-dimensional (3-D) active contour model to a 3-D ultrasonic data file for segmenting of the breast tumor. The 3-D ultrasonic file is composed of a series of two-dimensional (2-D) images. Most of traditional techniques of 2-D image segmentation will not use the information between adjacent images. To suit the property of the 3-D data, we introduce the concept of the 3-D stick, the 3-D morphologic process and the 3-D active contour model. The 3-D stick can get over the problem that the ultrasonic image is full of speckle noise and highlight the edge information in images. The 3-D morphologic process helps to determine the contour of the tumor and the resulting contour can be regarded as the initial contour of the active contour model. Finally, the 3-D active contour model will make the initial contour approach to the real contour of the tumor. However, there is emphasis on these 3-D techniques that they do not consist of a series of 2-D techniques. When they work, they will consider the horizontal, vertical and depth directions at the same time. The use of these 3-D techniques not only segments the 3-D shape but also obtains the volume of the tumor. The volume of the tumor calculated by the proposed method will be compared with the volume calculated by the VOCAL software with the physician's manually drawn shape and it shows that the performance of our method is satisfactory. <s> BIB082 </s> Ultrasound image segmentation: a survey <s> C. Priors 1) Image Features: <s> In this work a new statistic deformable model for 3D segmentation of anatomical organs in medical images is proposed. A statistic discriminant snake performs a supervised learning of the object boundary in an image slice to segment the next slice of the image sequence. Each part of the object boundary is projected in a feature space generated by a bank of Gaussian filters. Then, clusters corresponding to different boundary pieces are constructed by means of linear discriminant analysis. Finally, a parametric classifier is generated from each contour in the image slice and embodied into the snake energy-minimization process to guide the snake deformation in the next image slice. The discriminant snake selects and classifies image features by the parametric classifier and deforms to minimize the dissimilarity between the learned and found image features. The new approach is of particular interest for segmenting 3D images with anisotropic spatial resolution, and for tracking temporal image sequences. In particular, several anatomical organs from different imaging modalities are segmented and the results compared to expert tracings. <s> BIB083 </s> Ultrasound image segmentation: a survey <s> C. Priors 1) Image Features: <s> B-mode images of 20 fresh postsurgical human liver samples were obtained to evaluate ultrasound ability in determining the grade of liver fibrosis. Image features derived from gray level concurrence and nonseparable wavelet transform were extracted to classify fibrosis with a classifier known as the support vector machine. Each liver sample subsequently underwent histologic examination and liver fibrosis was graded from 0 to 5 (i.e., six grades total). The six grades were then combined into two, three, four and six classes. Classifications with the extracted image features by the support vector machine were tested and correlated with histology. The results revealed that the best classification accuracy of two, three, four and six classes were 91%, 85%, 81% and 72%, respectively. Thus, liver fibrosis can be noninvasively characterized with B-mode ultrasound, even though the performance declines as the number of classes increases. The elastic constants of 16 samples out of a total of 20 were also correlated with the image features. The Pearson correlation coefficients indicated that the image features are more strongly correlated with the fibrosis grade than with the elastic constant. <s> BIB084 </s> Ultrasound image segmentation: a survey <s> C. Priors 1) Image Features: <s> The purpose of this study was to apply a novel method of multiscale echo texture analysis for distinguishing benign (hemangiomas) from malignant (hepatocellular carcinomas (HCCs) and metastases) focal liver lesions in B-mode ultrasound images. In this method, regions of interest (ROIs) extracted from within the lesions were decomposed into subimages by wavelet packets. Multiscale texture features that quantify homogeneity of the echogenicity were calculated from these subimages and were combined by an artificial neural network (ANN). A subset of the multiscale features was selected that yielded the highest performance in the classification of lesions measured by the area under the receiver operating characteristic curve (Az). In an analysis of 193 ROIs consisting of 50 hemangiomas, 87 hepatocellular carcinomas and 56 metastases, the multiscale features yielded a high A: value of 0.92 in distinguishing benign from malignant lesions, 0.93 in distinguishing hemangiomas from HCCs and 0.94 in distinguishing hemangiomas from metastases. Our new multiscale texture analysis method can effectively differentiate malignant from benign lesions, and thus has the potential to increase the accuracy of diagnosis of focal liver lesions in ultrasound images. <s> BIB085 </s> Ultrasound image segmentation: a survey <s> C. Priors 1) Image Features: <s> Classification of breast masses in greyscale ultrasound images is undertaken using a multiparameter approach. Five parameters reflecting the non-Rayleigh nature of the backscattered echo were used. These parameters, based mostly on the Nakagami and K distributions, were extracted from the envelope of the echoes at the site, boundary, spiculated region and shadow of the mass. They were combined to create a linear discriminant. The performance of this discriminant for the classification of breast masses was studied using a data set consisting of 70 benign and 29 malignant cases. The Az value for the discriminant was 0.96 ± 0.02, showing great promise in the classification of masses into benign and malignant ones. The discriminant was combined with the level of suspicion values of the radiologist leading to an Az value of 0.97 ± 0.014. The parameters used here can be calculated with minimal clinical intervention, so the method proposed here may therefore be easily implemented in an automated fashion. These results also support the recent reports suggesting that ultrasound may help as an adjunct to mammography in breast cancer diagnostics to enhance the classification of breast masses. <s> BIB086 </s> Ultrasound image segmentation: a survey <s> C. Priors 1) Image Features: <s> There are indications that the morphology of atherosclerotic carotid plaques, obtained by high-resolution ultrasound imaging, has prognostic implications. The objective of this study was to develop a computer-aided system that will facilitate the characterization of carotid plaques for the identification of individuals with asymptomatic carotid stenosis at risk of stroke. A total of 230 plaque images were collected which were classified into two types: symptomatic because of ipsilateral hemispheric symptoms, or asymptomatic because they were not connected with ipsilateral hemispheric events. Ten different texture feature sets were extracted from the manually segmented plaque images using the following algorithms: first-order statistics, spatial gray level dependence matrices, gray level difference statistics, neighborhood gray tone difference matrix, statistical feature matrix, Laws texture energy measures, fractal dimension texture analysis, Fourier power spectrum and shape parameters. For the classification task a modular neural network composed of self-organizing map (SOM) classifiers, and combining techniques based on a confidence measure were used. Combining the classification results of the ten SOM classifiers inputted with the ten feature sets improved the classification rate of the individual classifiers, reaching an average diagnostic yield (DY) of 73.1%. The same modular system was implemented using the statistical k-nearest neighbor (KNN) classifier. The combined DY for the KNN system was 68.8%. The results of this paper show that it is possible to identify a group of patients at risk of stroke based on texture features extracted from ultrasound images of carotid plaques. This group of patients may benefit from a carotid endarterectomy whereas other patients may be spared from an unnecessary operation. <s> BIB087 </s> Ultrasound image segmentation: a survey <s> C. Priors 1) Image Features: <s> Ovarian masses are a common phenomenon among women of all ages. The importance of prompt diagnosis of ovarian malignancies is obvious, due to the high mortality rate and the difficulty to detect a tumor in its early stages. In this work, an automatic technique for quantitative analysis and malignancy detection of ovarian masses using B-scan ultrasound (US) images is presented. The core of the technique is morphologic analysis of the ovarian mass. The method employed for this task is divided into two major stages: initial classification of the mass (into one of the three major tumor types: cyst, semisolid, solid), and detailed analysis of the mass. Malignancy evaluation is performed based on the collected data and the criteria provided by commonly used scoring systems. The results reflect adequate performance of the automatic method developed (referring to clinical requirements). <s> BIB088 </s> Ultrasound image segmentation: a survey <s> C. Priors 1) Image Features: <s> A new system for prostate diagnostics based on multifeature tissue characterization is proposed. Radiofrequency (RF) ultrasonic echo data are acquired during the standard transrectal ultrasound (US) imaging examination. Nine spectral, texture, first order and morphologic parameters are calculated and fed into two adaptive neuro-fuzzy inference systems (FIS) working in parallel. The outputs of the FISs are fed into a postprocessing procedure evaluating contextual information before being combined to form a malignancy map in which areas of high cancer probability are marked in red. The malignancy map is presented to the physician during the examination to improve the early detection of prostate cancer. The system has been evaluated on 100 patients undergoing radical prostatectomy. The ROC curve area using leave-one-out cross-validation over patients is A(Z) = 0.86 when distinguishing between hyperechoic and hypoechoic tumors and normal tissue and A(Z) = 0.84 when distinguishing between isoechoic tumors and healthy tissue, respectively. Tumors that are not visible in the conventional B-mode image can be located. Diagnosis of the prostate carcinoma using multifeature tissue characterization in combination with US imaging allows the detection of tumors at an early stage. Also, biopsy guidance and therapy planning can be improved. <s> BIB089 </s> Ultrasound image segmentation: a survey <s> C. Priors 1) Image Features: <s> Presents a statistical shape model for the automatic prostate segmentation in transrectal ultrasound images. A Gabor filter bank is first used to characterize the prostate boundaries in ultrasound images in both multiple scales and multiple orientations. The Gabor features are further reconstructed to be invariant to the rotation of the ultrasound probe and incorporated in the prostate model as image attributes for guiding the deformable segmentation. A hierarchical deformation strategy is then employed, in which the model adaptively focuses on the similarity of different Gabor features at different deformation stages using a multiresolution technique, i.e., coarse features first and fine features later. A number of successful experiments validate the algorithm. <s> BIB090 </s> Ultrasound image segmentation: a survey <s> C. Priors 1) Image Features: <s> Breast cancer is the most frequently diagnosed malignancy and the second leading cause of mortality in women . In the last decade, ultrasound along with digital mammography has come to be regarded as the gold standard for breast cancer diagnosis. Automatically detecting tumors and extracting lesion boundaries in ultrasound images is difficult due to their specular nature and the variance in shape and appearance of sonographic lesions. Past work on automated ultrasonic breast lesion segmentation has not addressed important issues such as shadowing artifacts or dealing with similar tumor like structures in the sonogram. Algorithms that claim to automatically classify ultrasonic breast lesions, rely on manual delineation of the tumor boundaries. In this paper, we present a novel technique to automatically find lesion margins in ultrasound images, by combining intensity and texture with empirical domain specific knowledge along with directional gradient and a deformable shape-based model. The images are first filtered to remove speckle noise and then contrast enhanced to emphasize the tumor regions. For the first time, a mathematical formulation of the empirical rules used by radiologists in detecting ultrasonic breast lesions, popularly known as the "Stavros Criteria" is presented in this paper. We have applied this formulation to automatically determine a seed point within the image. Probabilistic classification of image pixels based on intensity and texture is followed by region growing using the automatically determined seed point to obtain an initial segmentation of the lesion. Boundary points are found on the directional gradient of the image. Outliers are removed by a process of recursive refinement. These boundary points are then supplied as an initial estimate to a deformable model. Incorporating empirical domain specific knowledge along with low and high-level knowledge makes it possible to avoid shadowing artifacts and lowers the chance of confusing similar tumor like structures for the lesion. The system was validated on a database of breast sonograms for 42 patients. The average mean boundary error between manual and automated segmentation was 6.6 pixels and the normalized true positive area overlap was 75.1%. The algorithm was found to be robust to 1) variations in system parameters, 2) number of training samples used, and 3) the position of the seed point within the tumor. Running time for segmenting a single sonogram was 18 s on a 1.8-GHz Pentium machine. <s> BIB091 </s> Ultrasound image segmentation: a survey <s> C. Priors 1) Image Features: <s> A methodology is presented for constructing a statistical atlas of spatial distribution of prostate cancer from a large patient cohort, and it is used for optimizing needle biopsy. An adaptive-focus deformable model is used for the spatial normalization and registration of 100 prostate histological samples, which were provided by the Center for Prostate Disease Research of the US Department of Defense, resulting in a statistical atlas of spatial distribution of prostate cancer. Based on this atlas, a statistical predictive model was developed to optimize the needle biopsy sites, by maximizing the probability of detecting cancer. Experimental results using cross-validation show that the proposed method can detect cancer with a 99% success rate using seven needles, in these samples. <s> BIB092 </s> Ultrasound image segmentation: a survey <s> C. Priors 1) Image Features: <s> In this paper, a fully automatic method for luminal contour segmentation in intracoronary ultrasound imaging is introduced. Its principle is based on a contour with a priori properties that evolves according to the statistics of the ultrasound texture brightness, which is generally Rayleigh distributed. The main interest of the technique is its fully automatic character. This is insured by an initial contour that is not set by the user, like in classical snake-based algorithms, but estimated and, thus, adapted to each image. Its estimation combines two pieces of information extracted from the a posteriori probability function of the contour position: the function maximum location (or maximum a posteriori estimator) and the first zero-crossing of its derivative. Then, starting from the initial contour, a region of interest is automatically selected and the process iterated until the contour evolution can be ignored. In vivo coronary images from 15 patients, acquired with the 20-MHz central frequency Jomed Invision ultrasound scanner, were segmented with the developed method. Automatic contours were compared to those manually drawn by two physicians in terms of mean absolute difference. The results demonstrate that the error between automatic contours and the average of manual ones is of small amplitude, and only very slightly higher (0.099 +/- 0.032 mm) than the interexpert error (0.097 +/- 0.027 mm). <s> BIB093 </s> Ultrasound image segmentation: a survey <s> C. Priors 1) Image Features: <s> Prostate boundary detection from ultrasound images plays an important role in prostate disease diagnoses and treatments. However, due to the low contrast, speckle noise and shadowing in ultrasound images, this still remains a difficult task. Currently, prostate boundary detection is performed manually, which is arduous and heavily user dependent. A possible solution is to improve the efficiency by automating the boundary detection process with minimal manual involvement. This paper presents a new approach based on the level set method to automatically detect the prostate surface from 3D transrectal ultrasound images. The user interaction in the initialization procedure is relieved by automatically putting the centroid of the initial zero level sets close to the image center. Region information, instead of the image gradient, is integrated into the level set method to remedy the "boundary leaking" problem caused by gaps or weak boundaries. Moreover, to increase the accuracy and robustness, knowledge-based features, such as expected shape (kidney-like) and ultrasound appearance of the prostate (looking from within the gland, the intensities are transitions from dark to light), are also incorporated into the model. The proposed method is applied to eight 3D TRUS images and the results have shown its effectiveness. <s> BIB094 </s> Ultrasound image segmentation: a survey <s> C. Priors 1) Image Features: <s> We are developing computer vision techniques for the characterization of breast masses as malignant or benign on radiologic examinations. In this study, we investigated the computerized characterization of breast masses on three-dimensional (3-D) ultrasound(US) volumetric images. We developed 2-D and 3-D active contour models for automated segmentation of the mass volumes. The effect of the initialization method of the active contour on the robustness of the iterative segmentation method was studied by varying the contour used for its initialization. For a given segmentation, texture and morphological features were automatically extracted from the segmented masses and their margins. Stepwise discriminant analysis with the leave-one-out method was used to select effective features for the classification task and to combine these features into a malignancy score. The classification accuracy was evaluated using the area A z under the receiver operating characteristic (ROC) curve, as well as the partial area index A z (0.9) , defined as the relative area under the ROC curve above a sensitivity threshold of 0.9. For the purpose of comparison with the computer classifier, four experienced breast radiologists provided malignancy ratings for the 3-D US masses. Our dataset consisted of 3-D US volumes of 102 biopsied masses (46 benign, 56 malignant). The classifiers based on 2-D and 3-D segmentation methods achieved test A z values of 0.87±0.03 and 0.92±0.03, respectively. The difference in the A z values of the two computer classifiers did not achieve statistical significance. The A z values of the four radiologists ranged between 0.84 and 0.92. The difference between the computer’s A z value and that of any of the four radiologists did not achieve statistical significance either. However, the computer’s A z (0.9) value was significantly higher than that of three of the four radiologists. Our results indicate that an automated and effective computer classifier can be designed for differentiating malignant and benign breast masses on 3-D US volumes. The accuracy of the classifier designed in this study was similar to that of experienced breast radiologists. <s> BIB095 </s> Ultrasound image segmentation: a survey <s> C. Priors 1) Image Features: <s> Automatic prostate segmentation in ultrasound images is a challenging task due to speckle noise, missing boundary segments, and complex prostate anatomy. One popular approach has been the use of deformable models. For such techniques, prior knowledge of the prostate shape plays an important role in automating model initialization and constraining model evolution. In this paper, we have modeled the prostate shape using deformable superellipses. This model was fitted to 594 manual prostate contours outlined by five experts. We found that the superellipse with simple parametric deformations can efficiently model the prostate shape with the Hausdorff distance error (model versus manual outline) of 1.32/spl plusmn/0.62 mm and mean absolute distance error of 0.54/spl plusmn/0.20 mm. The variability between the manual outlinings and their corresponding fitted deformable superellipses was significantly less than the variability between human experts with p-value being less than 0.0001. Based on this deformable superellipse model, we have developed an efficient and robust Bayesian segmentation algorithm. This algorithm was applied to 125 prostate ultrasound images collected from 16 patients. The mean error between the computer-generated boundaries and the manual outlinings was 1.36/spl plusmn/0.58 mm, which is significantly less than the manual interobserver distances. The algorithm was also shown to be fairly insensitive to the choice of the initial curve. <s> BIB096 </s> Ultrasound image segmentation: a survey <s> C. Priors 1) Image Features: <s> Knowing the location and the volume of the prostate is important for ultrasound-guided prostate brachytherapy, a commonly used prostate cancer treatment method. The prostate boundary must be segmented before a dose plan can be obtained. However, manual segmentation is arduous and time consuming. This paper introduces a semi-automatic segmentation algorithm based on the dyadic wavelet transform (DWT) and the discrete dynamic contour (DDC). A spline interpolation method is used to determine the initial contour based on four user-defined initial points. The DDC model then refines the initial contour based on the approximate coefficients and the wavelet coefficients generated using the DWT. The DDC model is executed under two settings. The coefficients used in these two settings are derived using smoothing functions with different sizes. A selection rule is used to choose the best contour based on the contours produced in these two settings. The accuracy of the final contour produced by the proposed algorithm is evaluated by comparing it with the manual contour outlined by an expert observer. A total of 114 2D TRUS images taken for six different patients scheduled for brachytherapy were segmented using the proposed algorithm. The average difference between the contour segmented using the proposed algorithm and the manually outlined contour is less than 3 pixels. <s> BIB097 </s> Ultrasound image segmentation: a survey <s> C. Priors 1) Image Features: <s> The precise tomographic assessment of coronary artery disease by intravascular ultrasound (IVUS) is useful in quantitative studies. Such studies require identification of luminal and medial-adventitial (MA) borders in a sequence of IVUS images. We have developed a three-dimensional (3D) active-surface system for border detection that facilitates the analysis of many images with minimal user interaction. To assess the validity of the technique, luminal and MA borders in 529 end-diastolic images from nine coronary arterial segments (58.8 ± 14.2 images per patient) were traced manually by four experienced observers. The computer-detected borders were compared with borders determined by the four observers using a modified Williams' index (WI), the ratio of inter-observer variability to computer-observer variability. While manual tracing required 49.2 ± 12.1 min for analysis, the analysis system identified luminal (R2 = 0.92) and MA borders (R2 = 0.97) in 13.8 ± 4.0 min, a decrease of 35.4 min (p < 0.000001). The computer minus observer differences in lumen area and MA area were −0.88 ± 0.90 and −0.07 ± 0.63 mm2. Therefore, the computer system underestimated both lumen and MA area, but this effect was very small in MA area. The WI values and 95% confidence intervals were 0.98 (0.89,1.06) for luminal border detection and 0.99 (0.95,1.04) for MA border detection. Plaque volume measurements, a common endpoint of clinical trials, also verified the accuracy of the technique (R2 = 0.98). The proposed 3D active-surface border detection system provides a faster and less-tedious alternative to manual tracing for assessment of coronary artery anatomy in vivo. <s> BIB098 </s> Ultrasound image segmentation: a survey <s> C. Priors 1) Image Features: <s> This paper presents a novel segmentation technique for extracting cavity contours from ultrasound images. The problem is first discretized by projecting equispaced radii from an arbitrary seed point inside the cavity toward its boundary. The distance of the cavity boundary from the seed point is modeled by the trajectory of a moving object. The motion of this moving object is assumed to be governed by a finite set of dynamical models subject to uncertainty. Candidate edge points obtained along each radius include the measurement of the object position and some false returns. The modeling approach enables us to use the interacting multiple model estimator along with a probabilistic data association filter, for contour extraction. The convergence rate of the method is very fast because it does not employ any numerical optimization. The robustness and accuracy of the method are demonstrated by segmenting contours from a series of ultrasound images. The results are validated through comparison with manual segmentations performed by an expert. An application of the method in segmenting bone contours from computed tomography images is also presented. <s> BIB099 </s> Ultrasound image segmentation: a survey <s> C. Priors 1) Image Features: <s> The paper presents a novel segmentation technique to extract prostate contours from transrectal ultrasound (TRUS) images. A sticks filter is first used to reduce the speckle and enhance the image contrast. The problem is then discretized by projecting equispaced radii from an arbitrary seed point inside the prostate cavity towards its boundary. The distance of the prostate boundary from the seed point is modeled by the trajectory of a moving object. The motion of this moving object is assumed to be governed by a finite set of dynamical models subject to uncertainty. Candidate edge points obtained along each radius include the measurement of the object position and some false returns. This modeling approach enables us to employ the interacting multiple model (IMM) estimator along with a probabilistic data association filter (PDAF) for prostate contour extraction. Since the method does not employ any numerical optimization, convergence is very fast. The robustness and accuracy of the method is demonstrated by segmenting contours from a series of prostate ultrasound images. <s> BIB100 </s> Ultrasound image segmentation: a survey <s> C. Priors 1) Image Features: <s> We present a technique for semiautomated segmentation of human prostates using suprapubic ultrasound (US) images. In this approach, a speckle reducing anisotropic diffusion (SRAD) is applied to enhance the images and the instantaneous coefficient of variation (ICOV) is utilized for edge detection. Segmentation is accomplished via a parametric active contour model in a polar coordinate system that is tailored to the application. The algorithm initially approximates the prostate boundary in two stages. First a primary contour is detected using an elliptical model, followed by a primary contour optimization using an area-weighted mean-difference binary flow geometric snake model. The algorithm was assessed by comparing the computer-derived contours with contours produced manually by three sonographers. The proposed method has application in radiation therapy planning and delivery, as well as in automated volume measurements for ultrasonic diagnosis. The average root mean square discrepancy between computed and manual outlines is less than the inter-observer variability. Furthermore, 76% of the computer-outlined contour is less than 1 sigma manual outline variance away from "true" boundary of prostate. We conclude that the methods developed herein possess acceptable agreement with manually contoured prostate boundaries and that they are potentially valuable tools for radiotherapy treatment planning and verification. <s> BIB101 </s> Ultrasound image segmentation: a survey <s> C. Priors 1) Image Features: <s> The instantaneous coefficient of variation (ICOV) edge detector, based on normalized gradient and Laplacian operators, has been proposed for edge detection in ultrasound images. In this paper, the edge detection and localization performance of the ICOV-squared (ICOVS) detector are examined. First, a simplified version of the ICOVS detector, the normalized gradient magnitude squared, is scrutinized in order to reveal the statistical performance of edge detection and localization in speckled ultrasound imagery. Both the probability of detection and the probability of false alarm are evaluated for the detector. Edge localization is characterized by the position of the peak and the 3-dB width of the detector response. Then, the speckle-edge response of the ICOVS as applied to a realistic edge model is studied. Through theoretical analysis, we reveal the compensatory effects of the normalized Laplacian operator in the ICOV edge detector for edge-localization error. An ICOV-based edge-detection algorithm is implemented in which the ICOV detector is embedded in a diffusion coefficient in an anisotropic diffusion process. Experiments with real ultrasound images have shown that the proposed algorithm is effective in extracting edges in the presence of speckle. Quantitatively, the ICOVS provides a lower localization error, and qualitatively, a dramatic improvement in edge-detection performance over an existing edge-detection method for speckled imagery. <s> BIB102 </s> Ultrasound image segmentation: a survey <s> C. Priors 1) Image Features: <s> In this paper, we report an active contour algorithm that is capable of using prior shapes. The energy functional of the contour is modified so that the energy depends on the image gradient as well as the prior shape. The model provides the segmentation and the transformation that maps the segmented contour to the prior shape. The active contour is able to find boundaries that are similar in shape to the prior, even when the entire boundary is not visible in the image (i.e., when the boundary has gaps). A level set formulation of the active contour is presented. The existence of the solution to the energy minimization is also established. ::: ::: We also report experimental results of the use of this contour on 2d synthetic images, ultrasound images and fMRI images. Classical active contours cannot be used in many of these images. <s> BIB103 </s> Ultrasound image segmentation: a survey <s> C. Priors 1) Image Features: <s> The objective of this study was to compare textural to attenuation/backscatter indices of fatty liver correlated to histology to suggest the better approach for an objective noninvasive ultrasonic "biopsy". Forty-four patients with severe steatosis by histopathology were selected for this study. Ten patients had "pure" fatty liver and 34 had in addition fibrosis and/or inflammation. Ultrasonic images were acquired before needle insertion. The ROI used for biopsy was marked on the ultrasonic image and characterized by three attenuation/backscatter and 18 textural related indices. Statistical analysis was performed using logistic regression. Twenty-one healthy subjects served as control. The attenuation/backscatter indices were superior to textural indices in differentiating between the categories studied. Pure fatty livers could be reliably identified (AUC = 1, SE = 0). Among the 18 textural indices, "co-occurrences sum entropy" and "co-occurrences entropy" presented the best results. Attenuation/backscatter based indices appear to have better potential than the textural based indices. <s> BIB104 </s> Ultrasound image segmentation: a survey <s> C. Priors 1) Image Features: <s> Abstract Rationale and objectives To investigate the potential usefulness of computer-aided diagnosis as a tool for radiologists in the characterization and classification of mass lesions on ultrasound. Materials and methods Previously, a computerized method for the automatic classification of breast lesions on ultrasound was developed. The computerized method includes automatic segmentation of the lesion from the ultrasound image background and automatic extraction of four features related to lesion shape, margin, texture, and posterior acoustic behavior. In this study, the effectiveness of the computer output as an aid to radiologists in their ability to distinguish between malignant and benign lesions, and in their patient management decisions in terms of biopsy recommendation are evaluated. Six expert mammographers and six radiologists in private practice at an institution accredited by the American Ultrasound Institute of Medicine participated in the study. Each observer first interpreted 25 training cases with feedback of biopsy results, and then interpreted 110 additional ultrasound cases without feedback. Simulating an actual clinical setting, the 110 cases were unknown to both the observers and the computer. During interpretation, observers gave their confidence that the lesion was malignant and also their patient management recommendation (biopsy or follow-up). The computer output was then displayed, and observers again gave their confidence that the lesion was malignant and their patient management recommendation. Statistical analyses included receiver operator characteristic analysis and Student t -test. Results For the expert mammographers and for the community radiologists, the A z (area under the receiver operator characteristic curve) increased from 0.83 to 0.87 ( P = .02) and from 0.80 to 0.84 ( P = .04), respectively, when the computer aid was used in the interpretation of the ultrasound images. Also, the A z values for the community radiologists with aid and for the expert mammographers without aid are similar to the A z value for the computer alone (A z = 0.83). Conclusion Computer analysis of ultrasound images of breast lesions has been shown to improve the diagnostic accuracy of radiologists in the task of distinguishing between malignant and benign breast lesions and in recommending cases for biopsy. <s> BIB105 </s> Ultrasound image segmentation: a survey <s> C. Priors 1) Image Features: <s> RATIONALE AND OBJECTIVES ::: To develop and evaluate a two-stage computerized method that first detects suspicious regions on ultrasound images, and subsequently distinguishes among different lesion types. ::: ::: ::: MATERIALS AND METHODS ::: The first stage of detecting potential lesions was based on expected lesion shape and margin characteristics. After the detection stage, all candidate lesions were classified by a Bayesian neural net based on computer-extracted lesion features. Two separate tasks were performed and evaluated at the classification stage: the first classification task was the distinction between all actual lesions and false-positive detections; the second classification task was the distinction between actual cancer and all other detected lesion candidates (including false-positive detections). The neural nets were trained on a database of 400 cases (757 images), consisting of complex cysts and benign and malignant lesions, and tested on an independent database of 458 cases (1,740 images including 578 normal images). ::: ::: ::: RESULTS ::: In the distinction between all actual lesions and false-positive detections, Az values of 0.94 and 0.91 were obtained with the training and testing data sets, respectively. Sensitivity by patient of 90% at 0.45 false-positive detections per image was achieved for this detection-plus-classification scheme for the testing data set. Distinguishing cancer from all other detections (false-positives plus all benign lesions) proved to be more challenging, and Az values of 0.87 and 0.81 were obtained during training and testing, respectively. Sensitivity by patient of 100% at 0.43 false-positive malignancies per image was achieved in the detection and classification of cancerous lesions for the testing dataset. ::: ::: ::: CONCLUSION ::: The results show promising performance of the computerized lesion detection and classification method, and indicate the potential of such a system for clinical breast ultrasound. <s> BIB106 </s> Ultrasound image segmentation: a survey <s> C. Priors 1) Image Features: <s> Automatic contouring for breast tumors using medical ultrasound (US) imaging may assist physicians without relevant experience, in making correct diagnoses. This study integrates the advantages of neural network (NN) classification and morphological watershed segmentation to extract precise contours of breast tumors from US images. Textural analysis is employed to yield inputs to the NN to classify ultrasonic images. Autocovariance coefficients specify texture features to classify breasts imaged by US using a self-organizing map (SOM). After the texture features in sonography have been classified, an adaptive preprocessing procedure is selected by SOM output. Finally, watershed transformation automatically determines the contours of the tumor. In this study, the proposed method was trained and tested using images from 60 patients. The results of computer simulations reveal that the proposed method always identified similar contours and regions-of-interest (ROIs) to those obtained by manual contouring (by an experienced physician) of the breast tumor in ultrasonic images. As US imaging becomes more widespread, a functional automatic contouring method is essential and its clinical application is becoming urgent. Such a method provides robust and fast automatic contouring of US images. This study is not to emphasize that the automatic contouring technique is superior to the one undertaken manually. Both automatic and manual contours did not, after all, necessarily result in the same factual pathologic border. In computer-aided diagnosis (CAD) applications, automatic segmentation can save much of the time required to sketch a precise contour, with very high stability. <s> BIB107 </s> Ultrasound image segmentation: a survey <s> C. Priors 1) Image Features: <s> To increase the ability of ultrasonographic (US) technology for the differential diagnosis of solid breast tumors, we describe a novel computer-aided diagnosis (CADx) system using data mining with decision tree for classification of breast tumor to increase the levels of diagnostic confidence and to provide the immediate second opinion for physicians. Cooperating with the texture information extracted from the region of interest (ROI) image, a decision tree model generated from the training data in a top-down, general-to-specific direction with 24 co-variance texture features is used to classify the tumors as benign or malignant. In the experiments, accuracy rates for a experienced physician and the proposed CADx are 86.67% (78/90) and 95.50% (86/90), respectively. <s> BIB108 </s> Ultrasound image segmentation: a survey <s> C. Priors 1) Image Features: <s> Abstract This paper extends 2D active shape models to 2D+time by presenting a method for modeling and segmenting spatio-temporal shapes (ST-shapes). The modeling part consists of constructing a statistical model of ST-shape parameters. This model describes the principal modes of variation of the ST-shape in addition to constraints on the allowed variations. An active approach is used in segmentation where an initial ST-shape is deformed to better fit the data and the optimal proposed deformation is calculated using dynamic programming. Segmentation results on both synthetic and real data are presented. <s> BIB109 </s> Ultrasound image segmentation: a survey <s> C. Priors 1) Image Features: <s> Extraction of structures of interest in medical images is often an arduous task because of noisy or incomplete data. However, hand-segmented data are often available and most of the structures to be extracted have a similar shape from one subject to an other. Then, the possibility of modeling a family of shapes and restricting the new structure to be extracted within this class is of particular interest. This approach is commonly implemented using active shape models [2] and the definition of the image term is the most challenging component of such an approach. In parallel, level set methods [8] define a powerful optimization framework, that can be used to recover objects of interest by the propagation of curves or surfaces. They can support complex topologies, considered in higher dimensions, are implicit, intrinsic and parameter free. In this paper we re-visit active shape models and introduce a level set variant of them. Such an approach can account for prior shape knowledge quite efficiently as well as use data/image terms of various form and complexity. Promising results on the extraction of brain ventricles in MR images demonstrate the potential of our approach. <s> BIB110 </s> Ultrasound image segmentation: a survey <s> C. Priors 1) Image Features: <s> We present a morphological multi-scale method for image sequence processing, which results in a truly coupled spatio-temporal anisotropic diffusion. The aim of the method is not to smooth the level-sets of single frames but to denoise the whole sequence while retaining geometric features such as spatial edges and highly accelerated motions. This is obtained by an anisotropic spatio-temporal level-set evolution, where the additional artificial time variable serves as the multi-scale parameter. The diffusion tensor of the evolution depends on the morphology of the sequence, given by spatial curvatures of the level-sets and the curvature of trajectories (=acceleration) in sequence-time. We discuss different regularization techniques and describe an operator splitting technique for solving the problem. Finally we compare the new method with existing multi-scale image sequence processing methodologies. <s> BIB111 </s> Ultrasound image segmentation: a survey <s> C. Priors 1) Image Features: <s> This paper describes a new method for segmentation of fetal anatomic structures from echographic images. More specifically, we estimate and measure the contours of the femur and of cranial cross-sections of fetal bodies, which can thus be automatically measured. Contour estimation is formulated as a statistical estimation problem, where both the contour and the observation model parameters are unknown. The observation model (or likelihood function) relates, in probabilistic terms, the observed image with the underlying contour. This likelihood function is derived from a region-based statistical image model. The contour and the observation model parameters are estimated according to the maximum likelihood (ML) criterion, via deterministic iterative algorithms. Experiments reported in the paper, using synthetic and real images, testify for the adequacy and good performance of the proposed approach. <s> BIB112 </s> Ultrasound image segmentation: a survey <s> C. Priors 1) Image Features: <s> This study presents a geometric model and a computational algorithm for segmentation of ultrasound images. A partial differential equation (PDE)-based flow is designed in order to achieve a maximum likelihood segmentation of the target in the scene. The flow is derived as the steepest descent of an energy functional taking into account the density probability distribution of the gray levels of the image as well as smoothness constraints. To model gray level behavior of ultrasound images, the classic Rayleigh probability distribution is considered. The steady state of the flow presents a maximum likelihood segmentation of the target. A finite difference approximation of the flow is derived, and numerical experiments are provided. Results are presented on ultrasound medical images as fetal echography and echocardiography. <s> BIB113 </s> Ultrasound image segmentation: a survey <s> C. Priors 1) Image Features: <s> We propose a new spatio-temporal elastic registration algorithm for motion reconstruction from a series of images. The specific application is to estimate displacement fields from two-dimensional ultrasound sequences of the heart. The basic idea is to find a spatio-temporal deformation field that effectively compensates for the motion by minimizing a difference with respect to a reference frame. The key feature of our method is the use of a semi-local spatio-temporal parametric model for the deformation using splines, and the reformulation of the registration task as a global optimization problem. The scale of the spline model controls the smoothness of the displacement field. Our algorithm uses a multiresolution optimization strategy to obtain a higher speed and robustness. We evaluated the accuracy of our algorithm using a synthetic sequence generated with an ultrasound simulation package, together with a realistic cardiac motion model. We compared our new global multiframe approach with a previous method based on pairwise registration of consecutive frames to demonstrate the benefits of introducing temporal consistency. Finally, we applied the algorithm to the regional analysis of the left ventricle. Displacement and strain parameters were evaluated showing significant differences between the normal and pathological segments, thereby illustrating the clinical applicability of our method. <s> BIB114 </s> Ultrasound image segmentation: a survey <s> C. Priors 1) Image Features: <s> Segmentation of the left ventricle in echocardiographic images is a task with important diagnostic power. We propose a model-based approach that aims at extracting the left ventricle for each frame of the cardiac cycle. Our approach exhibits several novel elements. Modelling consists of two separate components, one for the systolic and one for the diastolic moment. Segmentation is considered in two steps. During the first step a linear combination of the systolic and the diastolic model is to be recovered – that dictates the new model – along with a similarity transformation that projects this model to the desired image features. During the second step, a linear combination of the modes of variation for the systolic and diastolic models is recovered for precise extraction of the endocardium boundaries. The process is considered in the temporal domain where constraints are introduced to couple information across frames and to lead to a smooth solution. Promising results demonstrate the potentials of the presented framework. <s> BIB115 </s> Ultrasound image segmentation: a survey <s> C. Priors 1) Image Features: <s> In this paper, a novel method for the boundary detection of human kidneys from three dimensional (3D) ultrasound (US) is proposed. The inherent difficulty of interpretation of such images, even by a trained expert, makes the problem unsuitable for classical methods. The method here proposed finds the kidney contours in each slice. It is a probabilistic Bayesian method. The prior defines a Markov field of deformations and imposes the restriction of contour smoothness. The likelihood function imposes a probabilistic behavior to the data, conditioned to the contour position. This second function, which is also Markov, uses an empirical model of distribution of the echographical data and a function of the gradient of the data. The model finally includes, as a volumetric extension of the prior, a term that forces smoothness along the depth coordinate. The experiments that have been carried out on echographies from real patients validate the model here proposed. A sensitivity analysis of the model parameters has also been carried out. <s> BIB116 </s> Ultrasound image segmentation: a survey <s> C. Priors 1) Image Features: <s> The feasibility of selecting fractal feature vector based on multiresolution analysis to segment suspicious abnormal regions of ultrasonic liver images is described in this paper. The proposed feature extraction algorithm is based on the spatial-frequency decomposition and fractal geometry. Segmentation of various liver diseases reveals that the fractal feature vector based on multiresolution analysis is trustworthy. A quantitative characterization based on the proposed unsupervised segmentation algorithm can be utilized to establish an automatic computer-aided diagnostic system. As well, to increase the visual interpretation capability of ultrasonic liver image for junior physicians, off-line learning software is developed to investigate the visual criteria. <s> BIB117 </s> Ultrasound image segmentation: a survey <s> C. Priors 1) Image Features: <s> This work presents a novel texture and shape priors based method for kidney segmentation in ultrasound (US) images. Texture features are extracted by applying a bank of Gabor filters on test images through a two-sided convolution strategy. The texture model is constructed via estimating the parameters of a set of mixtures of half-planed Gaussians using the expectation-maximization method. Through this texture model, the texture similarities of areas around the segmenting curve are measured in the inside and outside regions, respectively. We also present an iterative segmentation framework to combine the texture measures into the parametric shape model proposed by Leventon and Faugeras. Segmentation is implemented by calculating the parameters of the shape model to minimize a novel energy function. The goal of this energy function is to partition the test image into two regions, the inside one with high texture similarity and low texture variance, and the outside one with high texture variance. The effectiveness of this method is demonstrated through experimental results on both natural images and US data compared with other image segmentation methods and manual segmentation. <s> BIB118 </s> Ultrasound image segmentation: a survey <s> C. Priors 1) Image Features: <s> We present a coupled minimization problem for image segmentation using prior shape and intensity profile. One part of the model minimizes a shape related energy and the energy of geometric active contour with a parameter that balances the influence from these two. The minimizer corresponding to a fixed parameter in this minimization gives a segmentation and an alignment between the segmentation and prior shape. The second part of this model optimizes the selection of the parameter by maximizing the mutual information of image geometry between the prior and the aligned novel image over all the alignments corresponding to different parameters in the first part. By this coupling the segmentation arrives at higher image gradient, forms a shape similar to the prior, and captures the prior intensity profile. We also propose using mutual information of image geometry to generate intensity model from a set of training images. Experimental results on cardiac ultrasound images are presented. These results indicate that the proposed model provides close agreement with expert traced borders, and the parameter determined in this model for one image can be used for images with similar properties. <s> BIB119 </s> Ultrasound image segmentation: a survey <s> C. Priors 1) Image Features: <s> Extraction of blood vessel boundaries from intravascular ultrasound images is essential in the quantitative analysis of cardiovascular functions. In this study, we are presenting a completely automated procedure for determining blood vessel borders. This approach uses textural operators to separate different tissue regions and morphological processing to refine extracted contours. The method was tested in a set of 29 intravascular ultrasound images obtainedin vivo. To assess the performance of the method, we have compared the automatically processed images with the manual tracings, using three different criteria: correlation coefficient, match ratio, and relative error of computed shape parameters. In both contour detection and shape parameters estimation, the proposed method yielded consistently good results. Due to its robustness and accuracy, this approach is appropriate for clinical use, whereas computational efficiency of the method facilitates low-cost implementation. <s> BIB120
• Gray level distribution: As noted in the previous section, the Rayleigh model of speckle has proved a popular choice. A Rayleigh distribution was used in the anisotropic diffusion edge detection method of  and in statistical segmentation methods in  , BIB039 , BIB093 , BIB040 ,  , and BIB112 . Most recently the Rayleigh distribution was incorporated into the level set method of Sarti et al. BIB113 and of Cardinal et al. BIB114 . A shifted Rayleigh distribution was used in the active contour approach of BIB041 , BIB050 and in level set segmentation algorithm in  and BIB042 . Other gray level distribution models have also been used in the ultrasound segmentation literature: for instance, the Gaussian  , BIB011 , BIB061 , BIB042 , BIB051 , BIB115 BIB062 BIB094 , exponential BIB115 , Gamma BIB078 , and Beta BIB116 distributions. • Intensity gradient (and higher derivatives): The motivation for using intensity gradient as a feature comes from the computer vision literature where based on a photometric model, high intensity gradients or equivalently intensity step changes/discontinuities in intensity are frequently associated with edges of objects. In ultrasound segmentation, it is, therefore, appropriate to use intensity gradient as a segmentation constraint if the goal is to find acoustic (impedance) discontinuities. Strictly speaking, one is also assuming that there is an approximately constant intensity on either side of the boundary (i.e., speckle has low amplitude). It has proved a popular constraint with or without prior speckle reduction, for example, in  , BIB015 , BIB012 , BIB052 ,  , BIB095 , BIB096 ,  ,  ,  BIB097 BIB043 , BIB116 , BIB053 , BIB063 , BIB013 , BIB044 , BIB032 BIB045 BIB098 , BIB024 , and BIB025 . Radial gradient estimation was also used in BIB003 , BIB099 , BIB100 , BIB054 , and BIB033 . To reduce the effect of speckle before gradient estimation, median filtering BIB064 , morphological smoothing BIB079 , speckle reduction BIB096 , BIB043 , BIB101 , BIB102 , coarse-to-fine optimization  , BIB062 , BIB011 , BIB051 , thresholding based adaptive smoothing BIB025 , BIB064 , and other prefiltering techniques BIB095 , BIB009 , BIB044 , BIB020 have been used. Gradient based level set algorithms have also been the most popular BIB079 , BIB080 , BIB103 , BIB065 , BIB046 ,  ,  and only few have used region information BIB113 , BIB101 ,  , BIB042 , BIB094 or a combination of both BIB062 , BIB119 , BIB081 , BIB114 . The limitations of the choice relate to the anisotropy of image acquisition, as the strength of a boundary response/edge is a function of the relative orientation of the transducer to the boundary, signal attenuation with depth reduces the strength of boundaries for deep objects, and speckle gives a strong intensity gradient response. Thus, intensity gradient information has only really proved a useful constraint for analyzing good quality clinical images and used in conjunction with other features, as mentioned above, as well as in BIB082 , BIB101 , BIB034 , BIB116 , BIB083 , and BIB064 . • Phase: The local phase provides an alternative way to characterize structure in an image which has been used for example in BIB047   BIB066 BIB067 . Measuring local phase, or rather phase congruency over spatial scales, provides a way to characterize different intensity features in terms of shape of the intensity profile rather than the intensity derivative magnitude; for example, a step edge has a phase value of 0 or , an intensity ridge has a phase of . Thus, phase has been suggested as a more robust feature for acoustic boundary detection. Speckle also has a phase signature so appropriate spatial scales have to be selected. Generally, phase is estimated using quadrature filter banks. Thus, there is a link between phase-based methods and other wavelet methods. • Similarity measures: Within the current discussion of -mode ultrasound segmentation, similarity measures have been used to provide a displacement estimate as a constraint in spatio-temporal analysis rather than a standalone segmentation feature (cf., use in image registration). The literature on ultrasound-ultrasound similarity measures is very small. The sum-of-squares difference (SSD) is used, for example, in BIB026 . Cohen and Dinstein developed an ultrasound-specific similarity measure based on the assumption that both image blocks contained speckle that could be modeled as a Rayleigh distributed multiplicative corruption BIB068 . This method was shown to be better than the SSD or normalized cross-correlation in  . A variant of Mutual information that uses neighborhood information, the mutual information of image geometry (MIIG) was proposed in BIB081 . The MIIG similarity was used for the estimation of an average intensity profile from a training set. This intensity prior was taken into account in the segmentation by means of the same similarity measure. • Texture measures: Although, ultrasound texture patterns are intrinsically dependent on the imaging system, they characterize the microstructure of the tissues being imaged. The distribution (coherent and/or random) of scatterers and their relative sizes to the wavelength of the incident ultrasound pulse, produces different texture patterns. As noted in the section on Speckle, ultrasound speckle statistics/measures have been successfully utilized to discriminate breast masses ([191] -  and references therein) and Bouhlel et al.  have recently proposed a MRF for textured ultrasound images based on the -distribution and on a multiplicative degradation model. However, general statistical texture analysis methods have been used with some success in this field. One potential advantage of texture analysis is that most texture measures are based on statistical patterns of the intensities and thus independent of the physics of the imaging system (see, for example,  and  ). Thus, in segmentation, where the aim is often to characterize the imaged object rather than necessarily characterize its true physical properties, texture analysis methods have proved successful. Texture characterization has been used, for instance, in classification and discrimination studies of liver tissues BIB002 BIB004 BIB016 BIB017 BIB018 BIB027 BIB084 BIB085 BIB104 BIB010 BIB055 breast masses BIB056 BIB069 BIB105 BIB070 BIB106 BIB071 ,  , BIB095 , BIB086 , [230]- [238] , carotid plaques BIB028 , BIB087 coronary plaques BIB029 , BIB048 , myocardial tissues BIB049 , BIB021 , the thyroid gland BIB001 , BIB057 , ovarian masses BIB088 , and prostate tissues BIB005 , BIB089 . In general, good classification rates have been obtained when large regions-of-interest are investigated (64 64 or 32 32 pixels). They are not as good with smaller sample sizes, i.e., when the pathological area is very small. Texture-based segmentation algorithms have also been proposed in the ultrasound literature and used in various clinical application domains such as: ovaries BIB006 , IVUS BIB120 , prostate BIB019 , BIB090 ,  , breast BIB091 , BIB107 , BIB030 , liver BIB058 , BIB117 , echocardiography BIB035 , and kidney BIB118 , BIB072 . Literally hundreds of texture measures have been used in the literature (statistical, structural, model based, transform based/spectral). Texture features derived from Haralick's co-occurrence matrices, although computationally expensive to compute, have performed well in a number of applications and have been used in combination with other measures or for comparison in a number of studies BIB035 , BIB071 , BIB095 , BIB051 , BIB002 BIB004 BIB016 BIB017 BIB018 BIB027 BIB084 , BIB104 , BIB007 BIB108  , BIB073 , BIB028 , BIB029 , BIB048 , BIB057 , BIB005 , BIB030 BIB022 BIB008 . Several authors have also emphasized the importance of a multiple resolution characterization (see e.g., BIB006 and BIB004 ) or the use of an intelligent parameters adjustment to the ultrasound resolution  . 2) Shape: Edge cues and region information are often not sufficient for a reliable and accurate segmentation. In this case, shape constraints are often found to effectively improve results. Because of attenuation, shadowing artefact and speckle, shape constraints have proved popular and successful recently in -mode ultrasound image segmentation BIB074 , BIB050 , BIB103 BIB119 BIB081 , BIB075 , BIB096 , BIB090 , BIB092 , BIB118 , BIB115 , BIB059 . Shape information is embedded in segmentation methods in several ways. Probably the most classical shape constraint involves boundary regularization, say as in the choice of the internal terms in an active contour. A second way to impose a shape constraint is by using a parametric shape and a preferred shape can be imposed, for example, in a probabilistic framework, using the learned distributions of the shape parameter over a set of training examples. An example of such a shape constraint used in ultrasound segmentation is in the work of Gong et al. BIB096 . A close variant is to impose a shape constraint by restricting the space of possible shapes to a predefined model shape with a set of transformations (global and/or local). This representation does not impose a parametrization of the shape and does not require training. In this case, the constraint is imposed on the transformation set, and global and local transformations may be penalized differently. See BIB050 for a good example of this. A widely and acknowledged alternative way to employ a shape constraint is via the use of a point distribution model (PDM) BIB014 . A PDM describes the average shape and the most characteristic shape variations of a set of training samples obtained using principal component analysis (PCA). The use of a PDM in segmentation is known as an active shape model (ASM). As in the previous model, no parametrization of the shape is needed. However, the set of possible shapes is defined by the principal modes of shape variation in the training set. In general, prior shape knowledge is modeled in a probabilistic framework. For instance, a Gaussian distribution BIB014 or a mixture model BIB023 . Although ASMs are suitable to describe free-form deformations, a point-to-point correspondence of the shape is necessary for the estimation of the average shape and its mode of variations. An extension to include intensity prior information was proposed, called the active appearance model (AAM) in BIB031 . Another extension is to include motion information as in BIB109 and the active appearance motion model (AAMM) in BIB075 . An extension of the AAM to 3-D was proposed by Bosch et al. BIB074 . An advantage of the AAM approach is that it attempts to learn both the geometric shape and the boundary model of an object. A limitation is that it assumes clinical images from different subjects have similar appearance. This can be difficult to achieve on routine clinical images due to natural subject-to-subject tissue property variations and operator-to-operator variation in acquisition. This led Bosch et al. to propose a nonlinear normalization step to reduce these effects. To overcome the limitation of point-to-point correspondence in the learning stage, Leventon et al. proposed to use a level set representation of the shape BIB076 . An example of the use of such a shape model on ultrasound images is provided by Xie et al. BIB118 . In order to allow more flexibility for the shape variation (i.e., avoid the Gaussian assumption mentioned above), a level set variant of the ASM, the Implicit ASM was proposed in BIB110 and applied to echocardiography in BIB115 . In this model, the evolving surface is not restricted within the modeled space, but only attracted to it using a distance function. To allow more flexibility, Chen et al. proposed, again in a level set framework, to constrain the solution using a distance function to the learned average shape (i.e., PCA is not used to learn the principal mode of variation) BIB103 . Prior intensity information was also taken into account in a similar way as in their recent work BIB119 , BIB081 . Finally, we would like to emphasise the fact that a shape model is only as good as the training samples from which it was built and the chosen shape-space model framework (e.g., linear, nonlinear). An important issue, still open to our knowledge, concerns the aptitude of shape constraints to handle disease cases. 3) Time: Ultrasound is often employed because it is realtime and, thus, the data available for segmentation is a temporal image sequence rather than a static frame. Therefore, it is sometimes useful to employ temporal priors in segmentation, i.e., consider segmentation as a spatio-temporal process. The most obvious example application is cardiac segmentation where the object moves in a periodic pattern. Respiration can also introduce (unwanted) motion. One way to handle motion is to register the data prior to segmentation. The other approach is to handle motion (correction or estimation) as part of the segmentation process directly via a temporal prior. Temporal priors or models can take a number of forms. The choice depends on how well the motion can be parameterized and whether computation speed is a determining factor in designing the segmentation method. We illustrate this with some example methods below. First temporal priors may be "weak" in the sense of insisting on global BIB015 or local  , BIB003 , BIB036 temporal coherence or smoothness. Another simple way to invoke a weak notion of temporal coherence is to propagate a segmentation result from one frame to initialize the segmentation in the next frame BIB079 ; a variant of this is to adjust the position of the initializing segmentation by using image velocity estimates BIB026 . A constant velocity model or other simple parametric model might be assumed to allow propagation of a result from one frame to initialize segmentation in another. A variant of this is to use a Kalman filter to blend a spatio-temporal model with feature measurements as in BIB060 and BIB077 . If the form of the parametric model is not obvious or motion patient-specific, then the temporal model may be estimated from data. For example, some authors have explicitly incorporated local estimates of image velocity BIB026 ,  , BIB047 , BIB037 . Malassiotis et al. use a temporal learning-filtering procedure to refine the output of active contour tracking BIB038 . In the work of Mitchell et al. BIB075 , the temporal model is learned from manually traced segmentations. Other authors have explicitly added time as an extra dimension to an -dimensional segmentation problem BIB074 ,  , BIB075 ,  , BIB051 . Only a few authors have considered solving the spatio-temporal segmentation problem where the space parameters are treated differently from the time parameter BIB036 , BIB111 .