Ultrasound image segmentation: a survey <s> V. CONCLUSION <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> BIB001 </s> Ultrasound image segmentation: a survey <s> V. CONCLUSION <s> Log compression of A lines to produce B-scan images in clinical ultrasound imaging systems is a standard procedure to control the dynamic range of the images. The statistics of such compressed images in terms of underlying scatterer statistics have not been derived. The statistics are analyzed for partially formed speckle using a general K distribution model of envelope statistics to derive the density function for the log-compressed envelope. This density function is used to elucidate the relation between the moments of the compressed envelope, the compression parameters, and the statistics of the scatterers. The analysis shows that the mean of the log-compressed envelope is an increasing function of both the backscattered energy and the effective scatterer density. The variance of the log-compressed envelope is a decreasing function of the effective scatterer density and is independent of the backscattered energy. <s> BIB002 </s> Ultrasound image segmentation: a survey <s> V. CONCLUSION <s> Image segmentation is the partition of an image into a set of nonoverlapping regions whose union is the entire image. The image is decomposed into meaningful parts which are uniform with respect to certain characteristics, such as gray level or texture. In this paper, we propose a methodology for evaluating medical image segmentation algorithms wherein the only information available is boundaries outlined by multiple expert observers. In this case, the results of the segmentation algorithm can be evaluated against the multiple observers' outlines. We have derived statistics to enable us to find whether the computer-generated boundaries agree with the observers' hand-outlined boundaries as much as the different observers agree with each other. We illustrate the use of this methodology by evaluating image segmentation algorithms on two different applications in ultrasound imaging. In the first application, we attempt to find the epicardial and endocardial boundaries from cardiac ultrasound images, and in the second application, our goal is to find the fetal skull and abdomen boundaries from prenatal ultrasound images. <s> BIB003 </s> Ultrasound image segmentation: a survey <s> V. CONCLUSION <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> BIB004 </s> Ultrasound image segmentation: a survey <s> V. CONCLUSION <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> BIB005 </s> Ultrasound image segmentation: a survey <s> V. CONCLUSION <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> BIB006 </s> Ultrasound image segmentation: a survey <s> V. CONCLUSION <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> BIB007 </s> Ultrasound image segmentation: a survey <s> V. CONCLUSION <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> BIB008 </s> Ultrasound image segmentation: a survey <s> V. CONCLUSION <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> BIB009 </s> Ultrasound image segmentation: a survey <s> V. CONCLUSION <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> BIB010 </s> Ultrasound image segmentation: a survey <s> V. CONCLUSION <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> BIB011
To conclude this paper, we have selected ten influential papers in the ultrasound segmentation literature. These are not claimed to be the "best" but have been selected based on criteria of the ultrasound-specific model they have employed and/or whether evaluation has been performed on a reasonable number of clinical datasets. The paper are listed in no particular order of importance. • Binder et al. (1999) BIB004 : although not a recent paper, this is a good illustration of the application of neural networks and learning to (cardiac) ultrasound image segmentation, which is also one of the few ultrasound segmentation methods that has been explicitly tested on a reasonable number of clinical images of varying quality. • BIB002 : statistics of log-compressed echo envelope using an underlying -distribution model before compression. Most ultrasound data comes from machines where log-compression has been done. Hence, it is important that the model takes into account this nonlinear transformation (speckle prior). • Mulet-Parada (2000) BIB006 : proposes an intensity-invariant image feature (local phase) for acoustic boundary and displacement estimation as an alternative to intensity derivatives (image prior). • Friedland et al. (1989) BIB001 : an early contribution viewing ultrasound segmentation as a spatio-temporal problem; (cardiac) boundary detection is solved as a minimization of an energy function with a Markovian spatio-temporal regularization. • BIB005 : and references therein. Presents a level set method for segmenting objects with missing boundaries (as frequently observed in ultrasound images). • and BIB008 , BIB009 : concerns the application and clinical validation of active appearance and active appearance motion modelling (intensity, shape, and motion priors). • Xie et al. (2005) BIB011 : presents an ultrasound segmentation method that combines texture and shape prior information in a level set framework (texture and shape prior). • BIB007 : a good illustration of employing an imaging physics prior (a shifted Rayleigh distribution to model gray level statistics), and a shape prior (a deformable template) to solve boundary estimation via a multiscale minimization. (gray level distribution and shape prior, probabilistic framework). • Chalana and Kim (1997) BIB003 : concerns the evaluation of boundary segmentation methods with respect to multiple expert delineations. In ultrasound segmentation, this is particularly important as there can be uncertainty in manual delineation. This is a quite widely employed method in echocardiographic image segmentation and used elsewhere in the medical image segmentation evaluation literature. • BIB010 : presents a 2-D contour segmentation approach where two contour models are combined in order to achieve smooth results and allow rapid changes in the boundary. This could be seen as an adaptive regularization of the boundaries because the models have different noise/smoothness tradeoff. Some final comments relating to open issues in this area. In this review, we have presented a snapshot view of the state-of-the-art in -mode ultrasound image segmentation as seen in the literature in the year 2005. First, (Section III) we have seen the need for more effort on segmentation validation to better understand the strengths and limitations of methods both through comparison with other methods in the literature and on larger (ideally standard) databases of data. This is important to encourage the adoption of methods in clinical practice. Even though ultrasound imaging is so widely used in clinical diagnosis and image-guided interventions, the field is far behind other areas of clinical image analysis, such as X-ray mammography or (rigid) image registration in this respect. Second, in Section IV, we have argued that successful ultrasound segmentation methods should utilize geometric, temporal, intensity, and imaging physics priors. The choice of which constraints to use is in part application specific, but many constraints hold for generic ultrasound image segmentation. Encouragingly, evidence in the recent literature points towards doing this in a more disciplined way, a trend which we hope this review will encourage other researchers to follow.