Ultrasound image segmentation: a survey <s> 1) Two-Dimensional Endocardial Border Detection: <s> A semiautomatic technique for isolating the ventricular endocardial border in echocardiograms from a commercially available two-dimensional phased array ultrasound system is presented. This method processes echo images using mathematical morphology to reduce the effects of range and azimuth variation inherent in echo. After morphological filtering, the endocardial border is extracted with traditional segmentation methods. Further processing of the resulting border using binary morphology produces a region of interest suitable for derivation of motion parameters of the endocardium. The area and the shape of semiautomatically-derived regions correlate well (r>0.93) with those defined by expert observers in a study of induced ischemia in seven canines. > <s> BIB001 </s> Ultrasound image segmentation: a survey <s> 1) Two-Dimensional Endocardial Border Detection: <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> BIB002 </s> Ultrasound image segmentation: a survey <s> 1) Two-Dimensional Endocardial Border Detection: <s> Abstract Image flow is a major source of three-dimensional information. This paper describes a new framework for computing image flow from time-varying imagery. In this framework, image-flow information is classified into two categories—conservation information and neighborhood information. Each type of information is recovered in the form of an estimate accompanied by a covariance matrix. Image flow is then computed by fusing the two estimates using estimation-theoretic techniques. This framework offers the following principal advantages. First, it allows estimation of certain types of discontinuous flow fields without any a priori knowledge about the location of discontinuities. The flow fields thus recovered are not blurred at motion boundaries. Second, covariance matrices (or alternatively, confidence measures) are associated with the estimate of image flow at each stage of computation. The estimation-theoretic nature of the framework and its ability to provide covariance matrices make it very useful in the context of applications such as incremental estimation of scene depth using techniques based on Kalman filtering. Finally, this framework serves to unify various existing approaches for image-flow estimation. In this paper, two algorithms based on this framework are described and are used to recover image flow from a variety of image sequences. To illustrate the usefulness of the framework in the context of an application, the image-flow estimates and their covariance matrices recovered from some representative image sequences are also used to recover scene depth. <s> BIB003 </s> Ultrasound image segmentation: a survey <s> 1) Two-Dimensional Endocardial Border Detection: <s> Abstract Many image analysis and computer vision problems have been expressed as the minimization of global energy functions describing the interactions between the observed data and the image representations to be extracted in a given task. In this note, we investigate a new comprehensive approach to minimize global energy functions using a multiscale relaxation algorithm. The energy function is minimized over nested subspaces of the original space of possible solutions. These subspaces consist of solutions which are constrained at different scales. The constrained relaxation is implemented via a coarse-to-fine multiresolution algorithm that yields fast convergence towards high quality estimates when compared to standard monoresolution or multigrid relaxation schemes. It also appears to be far less sensitive to local minima than standard relaxation algorithms. The efficiency of the approach is demonstrated on a highly nonlinear combinatorial problem which consists of estimating long-range motion in an image sequence on a discrete label space. The method is compared to standard relaxation algorithms on real world and synthetic image sequences. <s> BIB004 </s> Ultrasound image segmentation: a survey <s> 1) Two-Dimensional Endocardial Border Detection: <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> BIB005 </s> Ultrasound image segmentation: a survey <s> 1) Two-Dimensional Endocardial Border Detection: <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> BIB006 </s> Ultrasound image segmentation: a survey <s> 1) Two-Dimensional Endocardial Border Detection: <s> A method of segmenting echocardiography images based on three dimensional (two spatial and one time) surface was developed and tested on transcutaneous images from 38 patients and a four-dimensional (three spatial and one time) transesophageal scan of one patient. Active contour models with finite elements representing the curves in the imaging planes and finite difference description linking curves from time frame to time frame were employed. Region-based external forces were incorporated into the framework of the model and an analytical procedure for estimating the values of most of the model parameters was developed and used. <s> BIB007 </s> Ultrasound image segmentation: a survey <s> 1) Two-Dimensional Endocardial Border Detection: <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> BIB008 </s> Ultrasound image segmentation: a survey <s> 1) Two-Dimensional Endocardial Border Detection: <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> BIB009 </s> Ultrasound image segmentation: a survey <s> 1) Two-Dimensional Endocardial Border Detection: <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> BIB010 </s> Ultrasound image segmentation: a survey <s> 1) Two-Dimensional Endocardial Border Detection: <s> Abstract In this paper we present an evaluation of a robust visual image tracker on echocardiographic image sequences. We show how the tracking framework can be customized to define an appropriate shape space that describes heart shape deformations that can be learnt from a training data set. We also investigate energy-based temporal boundary enhancement methods to improve image feature measurement. Results are presented demonstrating real-time tracking on real normal heart motion data sequences and abnormal synthesized and real heart motion data sequences. We conclude by discussing some of our current research efforts. <s> BIB011 </s> Ultrasound image segmentation: a survey <s> 1) Two-Dimensional Endocardial Border Detection: <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> BIB012 </s> Ultrasound image segmentation: a survey <s> 1) Two-Dimensional Endocardial Border Detection: <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> BIB013 </s> Ultrasound image segmentation: a survey <s> 1) Two-Dimensional Endocardial Border Detection: <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> BIB014 </s> Ultrasound image segmentation: a survey <s> 1) Two-Dimensional Endocardial Border Detection: <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> BIB015 </s> Ultrasound image segmentation: a survey <s> 1) Two-Dimensional Endocardial Border Detection: <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> BIB016 </s> Ultrasound image segmentation: a survey <s> 1) Two-Dimensional Endocardial Border Detection: <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> BIB017 </s> Ultrasound image segmentation: a survey <s> 1) Two-Dimensional Endocardial Border Detection: <s> Displayed ultrasound (US) B-mode images often exhibit tissue intensity inhomogeneities dominated by nonuniform beam attenuation within the body. This is a major problem for intensity-based, automatic segmentation of video-intensity images because conventional threshold-based or intensity-statistic-based approaches do not work well in the presence of such image distortions. Time gain compensation (TGC) is typically used in standard US machines in an attempt to overcome this. However this compensation method is position-dependent which means that different tissues in the same TGC time-range (or corresponding depth range) will be, incorrectly, compensated by the same amount. Compensation should really be tissue-type dependent but automating this step is difficult. The main contribution of this paper is to develop a method for simultaneous estimation of video-intensity inhomogeities and segmentation of US image tissue regions. The method uses a combination of the maximum a posteriori (MAP) and Markov random field (MRF) methods to estimate the US image distortion field assuming it follows a multiplicative model while at the same time labeling image regions based on the corrected intensity statistics. The MAP step is used to estimate the intensity model parameters while the MRF step provides a novel way of incorporating the distributions of image tissue classes as a spatial smoothness constraint. We explain how this multiplicative model can be related to the ultrasonic physics of image formation to justify our approach. Experiments are presented on synthetic images and a gelatin phantom to evaluate quantitatively the accuracy of the method. We also discuss qualitatively the application of the method to clinical breast and cardiac US images. Limitations of the method and potential clinical applications are outlined in the conclusion. <s> BIB018 </s> Ultrasound image segmentation: a survey <s> 1) Two-Dimensional Endocardial Border Detection: <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> BIB019 </s> Ultrasound image segmentation: a survey <s> 1) Two-Dimensional Endocardial Border Detection: <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> BIB020 </s> Ultrasound image segmentation: a survey <s> 1) Two-Dimensional Endocardial Border Detection: <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> BIB021 </s> Ultrasound image segmentation: a survey <s> 1) Two-Dimensional Endocardial Border Detection: <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> BIB022 </s> Ultrasound image segmentation: a survey <s> 1) Two-Dimensional Endocardial Border Detection: <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> BIB023 </s> Ultrasound image segmentation: a survey <s> 1) Two-Dimensional Endocardial Border Detection: <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> BIB024 </s> Ultrasound image segmentation: a survey <s> 1) Two-Dimensional Endocardial Border Detection: <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> BIB025 </s> Ultrasound image segmentation: a survey <s> 1) Two-Dimensional Endocardial Border Detection: <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> BIB026 </s> Ultrasound image segmentation: a survey <s> 1) Two-Dimensional Endocardial Border Detection: <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> BIB027 </s> Ultrasound image segmentation: a survey <s> 1) Two-Dimensional Endocardial Border Detection: <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> BIB028 </s> Ultrasound image segmentation: a survey <s> 1) Two-Dimensional Endocardial Border Detection: <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> BIB029
The most popular approach has been to treat echocardiographic endocardial segmentation as a contour finding approach. This is not straightforward as the contrast around the border of the left ventricle chamber varies depending on the relative orientation of the border to the transducer direction, and attenuation. Thus conventional intensity gradient-based methods have had limited success on typical clinical images. Mishra et al. BIB024 proposed an active contour solution where the optimization was performed using a genetic algorithm. In a first image, low pass filtering and morphological operations were used to define an initial estimate of the contour. A nonlinear mapping of the intensity gradient was used in the energy functional which is minimized. The final contour was used to initialize contour finding in the next time frame. The convergence of the method was compared to solving the active contour using the conventional constrained quasi-Newton method. Manual delineations were done on 20 frames by two experts and the average compared to the automated algorithm to show that the intervariability between experts was similar to the difference between the manual and automated methods. The area correlation was found to be 0.92. This was a preliminary evaluation from which strong conclusions cannot be drawn. Mignotte and Meunier BIB012 chose to use a statistical external energy in a discrete active contour for the segmentation of short axis parasternal images, arguing that this was well-suited in ultrasound images with significant noise and missing boundaries. To this end, a shifted Rayleigh distribution was used to model gray levels statistics. The multiscale optimization strategy of Heitz et al. BIB004 was adapted to perform the energy minimization. The same optimization strategy was used for a maximum-likelihood (ML) region segmentation to extract a crude endocardial initial contour for the snake algorithm. Several segmentation results were shown and qualitatively good results were obtained. Mignotte et al. BIB015 proposed a boundary estimation algorithm, posed in a Bayesian framework where the prior information was modeled using deformable templates. This was a fully automatic unsupervised (i.e., no learning) approach. The estimation problem was formulated as a maximum a posteriori (MAP) optimization solved by means of a genetic algorithm. As in their previous work, BIB012 , the likelihood term relied on a shifted Rayleigh statistical model of the gray levels distribution. The template was defined by a circle along with a set of admissible affine global, nonaffine global, and local transformations. Illustrative results on simulated data and on a series of short axis images (50 frames) were shown. A comparison to manual delineations by two experts was also presented. The region-based segmentation method of Boukerroui et al.  follows a similar (Bayesian) methodological approach. Level sets are often considered as an alternative to active contours and this approach has also been considered for echocardiographic image segmentation. Yan et al. BIB025 considered applying the level set method to echocardiographic images using an adaptation of the fast marching method. To reduce errors attributed to using local feature (intensity gradient) measurements, they used an average intensity gradient-based measure in the speed term. The method was applied to a parasternal short axis and an apical four-chamber view sequence but the results were only discussed qualitatively. The latter example highlighted problems in using a purely edge-based level set method. Lin et al. BIB017 presented an interesting variant of the level set segmentation idea which combines edge and region information in a level set approach across spatial scales (a pyramid). This method assumes that a boundary is a closed curve. It was applied to rotational 3-D echocardiography data although the segmentation method was applied to individual 2-D slices. On 24 apical images, three experts manually delineated the borders of the left ventricle. Using the mean average difference between contours as the metric, they showed that the automatic method was within the variability of the manual delineations. Their method was said to work well when the quality of data was reasonably good but the method depends on being able to extract a contour at a high pyramid level. Extending their previous work on using prior shapes in a variational framework BIB027 , Chen et al. BIB029 , BIB026 used prior shape and intensity profiles to constrain the evolution of a geometric active contour. The problem was formulated as a coupled optimization problem where the prior intensity profiles were used to assist the estimation of the balance between the image information and the shape priors. The intensity profiles were model free as they were based on the maximization of a mutual information based criterion. Illustrative results were presented on long axis echocardiographic images. Klinger et al. presented a fairly classic approach to segmenting short-axis echocardiographic images using techniques based on mathematical morphology BIB001 . Data was time averaged to give a time-averaged cycle with reduced noise and then a series of morphological operators applied to identify the left ventricle cavity from which the border of the cavity could be found. This was tested on seven canine datasets where ischaemia had been induced in the animals to change heart motion. The area and shape correlation between the algorithm and an observer had . This is a relatively early paper and highlights issues at this time in terms of obtaining data (even on animals) of good enough quality to perform automated analysis. Segmentation of the left ventricle can also be considered a region-based or pixel-classification problem. In this case, the goal is to label pixels as either belonging to the blood pool or myocardium. Boukerroui et al.  , working in a Bayesian framework, presented a robust adaptive region segmentation. The adaptive property considers local class mean with a slow spatial variation, to compensate for the nonuniformity of ultrasound echo signals within the same tissue. The authors also used a multiple resolution implementation, as suggested by Ashton and Parker BIB005 , to justify the Gaussian probability density function (pdf) approximation at the lower resolution. The method was evaluated against manual delineation by an observer on a long-axis sequence and compared with another region-based segmentation method due to Xiao et al. BIB018 . Qualitative results were also shown on breast ultrasound data. Artificial neural network (ANN) based methods have been used for region-based segmentation  , BIB009 . For instance, Binder et al. BIB009 employed a two-layer backpropagation network that was trained by manually identifying 369 sample regions (7 7 pixels) from eight training images to segment the image as tissue or blood pool . Parameters (gray level mean and variance, contrast, entropy, and homogeneity from the co-occurrence matrices) were used in the ANN. Endocardial borders were found by first performing a radial search for candidate border points in single frames and then using a spatio-temporal contour linking step. The method was applied to end-diastolic and end-systolic parasternal SAX images from 38 patients with the data being of variable quality (12 good, 13 moderate, and 13 poor). This is one of the few studies that has explicitly looked at data of varying quality. Segmentation was successful in 34 of the 38 datasets. The automated method was compared with manual tracing by two experts. The ANN showed good correlation but from Bland-Altman analysis tended to overestimate the cavity areas. Technetium radionuclide ventriculography (Tc-RNV) was performed on 12 patients to enable the ejection fraction to be compared. The automated system showed a correlation of with Bland-Altman analysis showing that the ANN overestimated the ejection fraction. Over all the analyses, the best correlations were with the best data. 2) Spatio-Temporal Methods : It is natural to view echocardiographic segmentation as a spatio-temporal problem, since the object of interest moves (in a nonrigid way) and speckle decorrelates over the sequence. Spatio-temporal analysis potentially leads to better localization of a border, and gives a local estimate of image velocity as a side product of the segmentation. For instance, Friedland et al. BIB002 modeled the temporal continuity by means of a Markov random field (MRF). This is, to our knowledge, pioneering work that defined boundary detection as a minimization of an energy function with a Markovian spatio-temporal regularization. Polar coordinates were used for the contour definition and the simulated annealing (SA) algorithm was used for the optimization. Herlin and Ayache  used a spatio-temporal version of the Deriche edge detector in a method for tracking cardiac boundaries but their approach was not fully developed or validated in clinical practice. Mulet-Parada and Noble BIB013 proposed a local-phase-based approach to spatio-temporal endocardial border detection. They argue that local phase is a better basis for ultrasound-based feature detection and segmentation because local phase is theoretically invariant to intensity magnitude. They proposed using a derived measure from phase, called feature asymmetry for detecting the endocardium in a spatio-temporal space . They used the output of this feature detector (location, velocity, and confidence in measurement) as the feature vector in a motion tracking algorithm that was validated against manual delineations by a cardiologist. Local-phase has since been used in preference to intensity gradient for feature detection in a number of echocardiography studies   BIB019 BIB020 . A number of authors have used variations of the active contour concept to segment and track left ventricle boundaries. Chalana et al. developed a multiple active contour method to detect both the endocardium and epicardium in short-axis views BIB006 . They defined the left ventricle by an active surface model, which they reparameterized to show that the surface could be represented by two planar curves representing the endocardial and epicardial borders. They used the image intensity gradient as the attracting force and invoked temporal continuity via an external energy term that constrained the motion between consecutive frames. Their method was validated on 44 clinical datasets against manual delineations by four experts. The area correlation coefficient between their method and the average manual outline was 0.95 for the epicardium and 0.91 for the endocardium. They reported that the computer algorithm gives a lower boundary difference error for the epicardium than the endocardium. This may be due to the fact that their algorithm was initialized with a manual delineation of the epicardium. Kucera et al. BIB007 presented an example of applying 3-D active contours to echocardiographic data using a region-based external force. This is a relatively early work that used region information in a contour based segmentation approach. It can be compared to more recent level set based segmentation methods such as BIB017 and BIB028 . Mikic et al. proposed an active contour approach where propagation of a fitted contour from one frame to another was guided by optical flow estimates BIB008 provided by the method of Singh and Allen BIB003 (see also  ). This can be viewed as providing knowledge to guide the initialization of an active contour optimization. The image force in the active contour was defined in terms of a Gaussian smoothed intensity gradient. Parameters in the active contour were determined empirically. The method was evaluated on eight image sequences (three short axis, three long-axis, and two aortic root) and compared with 3-5 manual delineations. For endocardial border segmentation, the area correlation coefficient between their method and the average area was 0.99, 0.95, and 0.99 for three short axis sequences and the distance between the active contour and manual outlines was 1.15, 1.29, and 2.07 mm, respectively (see Fig. 1(a) for an example result taken from their paper). Setarehdan et al. developed a fuzzy multiscale edge detection (FMED) method for endocardial and epicardial border detection BIB010 that uses a wavelet transform to define the various levels of resolution of image content. In this work, an edge was defined as a point with maximal membership of the edge fuzzy set. Temporal information was included in edge detection by defining a moving edge fuzzy membership function, as well as an edge fuzzy membership function. The combined edge and motion membership function was then maximized to define edge points. This method was applied to SAX images where a radial-search-based strategy was used and the edge detection applied in one-dimensional (1-D) along rays from an automatically found center with some preprocessing restricting the search for the endocardial and endocardial borders. The method was evaluated on simulated data and 14 datasets from healthy subjects compared to delineations by a single expert. The correlation between the areas defined by the algorithm and the expert for the endocardial border was 0.96 and for the epicardial border was 0.95. The root mean square (rms) radial (sampled) distance between the algorithm and expert defined boundaries was found to be 1.84 mm (epicardium) and 3.5 mm (endocardium). Dias and Leitão  , proposed an algorithm for the joint endocardial and epicardial border estimation following the earlier work of Friedland and Adam BIB002 and Figueiredo and Leitão  . As in BIB002 and  polar coordinates were used for the contour representation. However, to our knowledge, this is the first approach which takes into account speckle statistics in boundary optimization. The estimation problem was modeled in a Bayesian framework as a MAP estimation problem with Rayleigh statistics for the data term and a spatio-temporal MRF for the regularization term. An iterative multigrid dynamic programming algorithm was used to solve the optimization problem. Results are given on simulated data and on one short axis sequence (half of a cardiac cycle). See Fig. 1(a) for an example result taken from their paper. Recently, Figueiredo et al. BIB014 , within the same framework, presented an unsupervised parametric contour segmentation based on -spline representations. A minimum description length (MDL) criterion was used to estimate the number of -spline knots. In contrast to  , where careful parameter tuning of the prior terms was required, this leads to an approach less dependent on parameter settings as the tradeoff between smoothness/robustness is optimal in the sense of MDL. Illustrative segmentation examples on a number of medical applications, including cardiac short-axis and intravascular ultrasound images were shown. Jacob et al. BIB011 BIB016 BIB021 developed a Kalman-filter based endocardial and epicardial border tracking method based on the dynamic contour tracking approach of Blake and Isard  . In this approach, a spatio-temporal contour model is constructed which has two parts; a shape-model constructed from manual delineations of the LV borders of a training set; and a motion model. An image measurement model is also defined (for instance, in BIB011 a 1-D local-phase based feature detector is used for endocardial border detection, and in BIB021 a wavelet-based method is used for epicardial border detection). A Kalman filter strategy is then used to combine the contour model with the image measurements. The approach was evaluated in a small case study on stress echocardiography data (four datasets: one 4C and three 2C) and the performance of the tracking and interpretation compared with manual border delineations using six quantitative geometric measures BIB021 . The area correlation coefficient was found to be 0. 98 . Bland-Altman analysis on endocardial/epicardial areas showed that endocardial border estimation was more accurate than epicardial border detection although the authors comment that strong conclusions can not be drawn from this as manual delineation is difficult to do. In BIB016 , Jacob et al. attempt to perform an evaluation against clinical scoring of border motion and myocardial thickening by cardiologists [ stress datasets (as used in BIB021 , two clinical observers)]. This is one of the few papers that has considered stress-echocardiography data, but as noted by the authors, only a limited number of datasets were analyzed, and the data quality, though typical of that seen in clinical practice was quite low. Bosch et al. BIB022 used an adaption of the active appearance model (AAM) approach that they called the active appearance motion model (AAMM) to represent the shape and appearance of the endocardium, as well as its motion (see Fig. 1(c) for an example result taken from their paper). An important step in their method was nonlinear intensity normalization. Their method was tested on 129 four-chamber datasets ( for training, for testing). An expert traced the borders of the images. Manual delineations were compared with the algorithm contours with an average distance error of 3.3 mm comparable with inter/intra observer variability for 97 of the data. Mitchell et al. BIB023 developed a 3-D AAM for segmenting four-chamber echocardiography data (2-D sequences with the third dimension time). The approach is spatio-temporal, i.e., adjusts itself in space and time. The model was learned from manually segmented examples in a training stage. The method was evaluated on the same data as used in BIB022 . The method was deemed successful in 57 of the 64 datasets. The endocardial average distance error was 3.9 mm which is slightly worse than the method of Bosch et al. BIB022 which they explain as being due in part to the extra degree of freedom but also to the fact that the 3-D method did not employ intensity normalization.