Ultrasound image segmentation: a survey <s> C. Prostate <s> This paper describes a method for segmenting transrectal ultrasound images of the prostate using feedforward neural networks. 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. Three neural network architectures are presented as examples and discussed. Each of these networks was trained using a small portion of a training image segmented by an expert sonographer. The results of applying the trained networks to the entire training image and to adjacent images in the two-dimensional image set are presented and discussed. The final network architecture was also trained with additional data from two other images in the set. The results of applying this retrained network to each of the images in the set are presented and discussed. <s> BIB001 </s> Ultrasound image segmentation: a survey <s> C. Prostate <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> BIB002 </s> Ultrasound image segmentation: a survey <s> C. Prostate <s> Describes a preprocessing algorithm for automated determination of object contours to determine changes in gray level in noisy images. The algorithm is illustrated for one-dimensional signals using different scales of resolution. Furthermore, a possibility for application of the proposed edge detector operator with multiple scales of resolution is described and illustrated. <s> BIB003 </s> Ultrasound image segmentation: a survey <s> C. Prostate <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> BIB004 </s> Ultrasound image segmentation: a survey <s> C. Prostate <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> BIB005 </s> Ultrasound image segmentation: a survey <s> C. Prostate <s> Abstract Objective: We investigated an algorithm to detect grey level transitions with multiple scales of resolution to improve edge detection and localisation in ultrasound images of the prostate. Introduction: We had developed a non-analytical operator for prostate contour determination implemented with minimum and maximum filters to identify and locate edges. We implemented a technique for improved determination of boundary parts in prostatic ultrasound images by adjusting the edge detection parameter to signal information. Methods: First the influence of prefilter settings and edge detection parameters is investigated in a test image and a real ultrasound image. Then, local standard deviation is used to identify more or fewer homogeneous regions that are filtered with course resolution, while areas with larger deviation indicate that grey level transitions occur, which should be preserved using smaller filter sizes to improve edge localisation. Results: Analysis of images with different filter sizes indicated that areas are merged for increasing filter sizes: less pronounced edges disappear or displace for larger filters. Two scales of resolution lead to an improved localisation of edges when smaller filter sizes are used in areas with an increased local standard deviation. Conclusions: This paper illustrates an edge detection method suitable as pre-processing step in interpretation of medical images. By adapting input parameters to signal information, object recognition can be applied in images from different imaging modalities. Also, disadvantages are discussed, resulting in a new application combining a localisation algorithm to find the initial contour and a delineation algorithm to improve the outlining of the resulting contour. <s> BIB006 </s> Ultrasound image segmentation: a survey <s> C. Prostate <s> This paper describes an approach to boundary detection in ultrasound speckle based on an image enhancement technique. The enhancement algorithm works by filtering the image with "sticks," short line segments which are varied in orientation to achieve the maximum projected value at each point. The statistical properties of this approach have been described in an earlier paper; in this work we present three significant extensions to improve the performance of the basic method. First, we investigate the effect of varying the size and shape of the sticks. We show that these variations affect the performance of the algorithm in very fundamental ways, for example by making it more or less sensitive to thinner or more tightly curving boundaries. Second, we present a means of improving the performance of this technique by estimating the distribution function of the orientation of the line passing through each point. Finally, we show that images can be "stained" for easier visual interpretation by applying to each pixel a false color whose hue is related to the orientation of the most prominent line segment at that point. Examples are given to illustrate the performance of the different settings on a single image. <s> BIB007 </s> Ultrasound image segmentation: a survey <s> C. Prostate <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> BIB008 </s> Ultrasound image segmentation: a survey <s> C. Prostate <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> BIB009 </s> Ultrasound image segmentation: a survey <s> C. Prostate <s> Abstract Intravascular ultrasound (IVUS) has established itself as a useful tool for coronary assessment. The vast amount of data obtained by a single IVUS study renders manual analysis impractical for clinical use. A computerized method is needed to accelerate the process and eliminate user-dependency. In this study, a new algorithm is used to identify the lumen border and the media-adventitia border (the external elastic membrane). Setting an initial surface on the IVUS catheter perimeter and using active contour principles, the surface inflates until virtual force equilibrium defined by the surface geometry and image features is reached. The method extracts these features in three dimensions (3-D). Eight IVUS procedures were performed using an automatic pullback device. Using the ECG signal for synchronization, sets of images covering the entire studied region and corresponding to the same cardiac phase were sampled. Lumen and media-adventitia border contours were traced manually and compared to the automatic results obtained by the suggested method. Linear regression results for vessel area enclosed by the lumen and media-adventitia border indicate high correlation between manual vs. automatic tracings ( y = 1.07 × −0.38; r = 0.98; SD = 0.112 mm 2 ; n = 88). These results indicate that the suggested algorithm may potentially provide a clinical tool for accurate lumen and plaque assessment. <s> BIB010 </s> Ultrasound image segmentation: a survey <s> C. Prostate <s> Segmentation of human prostate from ultrasound(US)images is a crucial step in radiation therapy, especially in real-time planning for USimage-guided prostate seed implant. This step is critical to determine the radioactive seed placement and to ensure the adequate dose coverage of prostate. However, due to the low contrast of prostate and very low signal-to-noise ratio in USimages, this task remains as an obstacle. The manual segmentation of this object is time consuming and highly subjective. In this work, we have proposed a three-dimensional (3D) deformable surface model for automatic segmentation of prostate. The model has a discrete structure made from a set of vertices in the 3D space that form triangle facets. The model converges from an initial shape to its equilibrium iteratively, by a weighted sum of the internal and external forces. Internal forces are based on the local curvature of the surface and external forces are extracted from the volumetric image data by applying an appropriate edge filter. We have also developed a method for initialization of the model from a few initial contours that are drawn on different slices. During the deformation, a resampling procedure is used to maintain the resolution of the model. The entire model is applied in a multiscale scheme, which increases the robustness and speed, and guarantees a better convergence. The model is tested on real clinical data and initial results are very promising. <s> BIB011 </s> Ultrasound image segmentation: a survey <s> C. Prostate <s> OBJECTIVE ::: Prostate diseases are very common in adult and elderly men, and prostate boundary detection from ultrasonographic images plays a key role in prostate disease diagnosis and treatment. However, because of the poor quality of ultrasonographic images, prostate boundary detection still remains a challenging task. Currently, this task is performed manually, which is arduous and heavily user dependent. To improve the efficiency by automating the boundary detection process, numerous methods have been proposed. We present a review of these methods, aiming to find a good solution that could efficiently detect the prostate boundary on ultrasonographic images. ::: ::: ::: METHODS ::: A full description of various methods is beyond the scope of this article; instead, we focus on providing an introduction to the different methods with a discussion of their advantages and disadvantages. Moreover, verification methods for estimating the accuracies of the algorithms reported in the literature are discussed as well. ::: ::: ::: RESULTS ::: From the investigation, we summarize several key issues that might be confronted and project possible future research. ::: ::: ::: CONCLUSIONS ::: Those model-based methods that minimize user involvement but allow for interactive guidance of experts will likely be most immediately successful. <s> BIB012 </s> Ultrasound image segmentation: a survey <s> C. Prostate <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> BIB013 </s> Ultrasound image segmentation: a survey <s> C. Prostate <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> BIB014 </s> Ultrasound image segmentation: a survey <s> C. Prostate <s> * Level set methods * Deformable models * Fast methods for implicit active contour models * Fast edge integration * Variational snake theory * Multiplicative denoising and deblurring * Total varation minimization for scalar/vector regularization * Morphological global reconstruction and levelings * Fast marching techniques for visual grouping and segmentation * Multiphase object detection and image segmentation * Adaptive segmentation of vector-valued images * Mumford-Shah for segmentation and stereo * Shape analysis toward model-based segmentation * Joint image registration and segmentation * Image alignment * Variational principles in optical flow estimation and tracking * Region matching and tracking under deformations or occlusions * Computational stereo * Visualization, analysis and shape reconstruction of sparse data * Variational problems and partial differential equations on implicit surfaces * Knowledge-based segmentation of medical images * Topology preserving geometric deformable models for brain reconstruction * Editing geometric models * Simulating natural phenomena <s> BIB015 </s> Ultrasound image segmentation: a survey <s> C. Prostate <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> BIB016 </s> Ultrasound image segmentation: a survey <s> C. Prostate <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> BIB017 </s> Ultrasound image segmentation: a survey <s> C. Prostate <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> BIB018 </s> Ultrasound image segmentation: a survey <s> C. Prostate <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> BIB019 </s> Ultrasound image segmentation: a survey <s> C. Prostate <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> BIB020 </s> Ultrasound image segmentation: a survey <s> C. Prostate <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> BIB021
Prostate cancer is the most commonly diagnosed cancer in adult and ageing men. Early detection and early intervention of progressive prostate cancer may help to reduce death rate. TRUS imaging has long been an essential tool for prostate cancer diagnosis. In fact, the combination of prostate specific antigen (PSA) testing with TRUS and digital rectal examination (DRE) has been responsible for diagnosing most prostate cancers in the United States each year. Prostate volumes and boundaries play an important role in diagnosis, treatment and followup of prostate cancer (see, for example, BIB012 ). In general, prostate boundaries are routinely outlined in transverse parallel 2-D slices along the length of the prostate. This has led to the development of a number of methods for (semi-) automatic detection of prostate boundaries. 2 An example of prostate segmentation from Gong et al. BIB016 is shown in Fig. 3 . Classical techniques have been proposed for prostate segmentation; derivative edge detection was used in  , and, nonlinear filtering (minimum/maximum filter) was proposed in BIB002 BIB003 BIB006 . Both supervised classification methods such as methods based on neural networks BIB001 and unsupervised pixel classifiers, for instance, using Laws texture features in a probabilistic framework BIB004 have also been proposed. These methods do not use shape modeling and knowledge of ultrasound physics and have undergone limited validation. Recent work has focused on incorporating prior information about shape and speckle models. For instance, Knoll et al.  proposed employing a parametrization of a snake based on a 1-D dyadic wavelet transform as a multiscale boundary curve analysis tool. The initialization of the snake used template matching between contour models of a training set and significant image edges. The authors used a 1-D wavelet based method in order to constrain the shape of the snake to evolve towards predefined models. The contour deformation method was integrated in a coarse-to-fine segmentation framework based on multiscale image edges represented by the modulus maxima of the 2-D dyadic wavelet transform. The fully automatic method was tested on 77 images from 11 patients against two experts. The analysis showed that shape information slightly improves results. However, no statistical analysis (of significance nor variance) was shown for the error comparison against the inter-observer variability. Ghanei et al. BIB011 designed a 3-D discrete deformable model to semi-automatically outline the prostate boundaries. Good segmentation results were obtained, in comparison to manual contours obtained slice-by-slice, subject to close initialization of the surface to the true boundaries. A 2-D semi-automatic discrete dynamic contour (DDC) model for prostate boundary segmentation was proposed by Ladak et al.  based on the Lobregt and Viergever model  . The same team extended their work to 3-D images in Hu et al.  . The underlying model is similar to that in Ghanei et al. BIB011 . The former used four (for 2-D, six for 3-D) manually selected points on specific locations of the prostate boundary and then applied an appropriate interpolation to complete the initial model. The latter used a few initial contours that were drawn on different slices. The specified points were generally clamped during the deformation of the DDC which uses some directional information (based on the fact that prostate tissue appears darker than the non prostate tissues in ultrasound). An editing tool was also provided to semi-automatically correct the segmentation results by specifying more boundary points and then running the DDC algorithm again. The accuracy of the 2-D and 3-D algorithm were assessed using global and local measures, however against only one expert radiologist. 117 images of 19 patients were used for the 2-D algorithm and six patients with an average of 24 slices, acquired using a rotational probe to form a 3-D data set, were used for the 3-D algorithm. The analysis showed that the semi-automatic segmentation was in agreement with the manual one except where the prostate boundaries were missing. Some other interesting conclusions can be drawn from this work. For example, 13% of cases were difficult to assess and the 3-D algorithm performed better that the 2-D one especially at the lateral margins of the prostate. Recently, Chiu et al. BIB017 , presented a variant of Ladak et al.'s  algorithm where the intensity gradient, used in the external energy force of the DDC model, was estimated using the dyadic spline wavelet transform. Pathak et al. BIB008 used the Sticks algorithm BIB007 followed by anisotropic edge-preserving smoothing based on the weakmembrane algorithm for semi-automatic boundary detection. A Canny edge detector was then used to construct an edge map rather than edges detected by the weak-membrane algorithm. This enabled prior knowledge-based filtering of the edge map to be used to detect only the relevant edges. The method was semiautomatic as user interaction was employed in the edge-linking stage. The experiments on 125 images from 16 patients with the help of five expert observers showed an increase of consistency in prostate delineation when automatically detected edges were used as a visual guide during the manual outlining process. In order to restrict user interaction, a deformable superellipse was employed as a parametric shape model for the prostate in a later work BIB016 . Superellipses are, on the one hand, attractive as only two pose parameters and four shape parameters need to be estimated, and on the other, as acknowledged by the authors, very restrictive as they can only model symmetric shapes. Apparently, this restriction was not a draw back for prostate segmentation as the fitting error was less than the inter-observer variability. The authors chose a Bayesian framework to solve the fitting problem and used their previous work BIB008 to estimate the edge likelihood and a training data set to estimate the prior distributions parameters of the superellipes shape parameters. The segmentation algorithm was semi-automatic as the user had to specify more than two points to give an approximate scale of the prostate. The algorithm was tested and analyzed under the same conditions as in BIB008 using the measures defined in BIB005 . The mean distance between the computer-generated boundaries and the average of the five manual outlines was significantly smaller than the average of the inter-observer variability (see Fig. 3 for an example result). Shen et al. BIB013 proposed a statistical shape model for automatic 2-D prostate boundaries detection. Their model takes into account the nature of TRUS images by considering the probe position in the shape normalization step and by making the image features descriptors invariant to probe rotation. Gabor filters at different orientations and scales were used for the data fidelity term and affine invariant features, which capture the prostate shape in a hierarchical fashion, were employed for the model term. The work presents several original contributions mainly in modeling the prior geometry of the prostate in the TRUS images. Modeling of the prostate boundaries by means of Gabor filters might be improved by taking into account the quadrature property of Gabor filters (see e.g., BIB009 and references therein). Their method was evaluated on eight images. This is a preliminary evaluation from which strong conclusions cannot be drawn. 3 The authors extended their work to segment 3-D data in  , and interestingly, they used a kernel support vector machine (KSVM) to estimate a probability density function of prostate tissue voxels based on the Gabor features. Unfortunately, the modification did not decrease the processing time. Recently, the authors proposed an efficient variant of their previous work by using Zernike moment based edge detection at the coarse resolutions and kept the SVM modeling only for the finest resolution. They also decreased the number of support vectors without a loss of accuracy  . The new algorithm ran ten times faster. Shen et al. BIB014 also worked on a 3-D statistical atlas of the spatial distribution of cancer for optimized prostate biopsy. One hundred prostate histological samples were used in the atlas construction. A 99% biopsy success rate was achieved using seven needles. Abolmaesumi and Sirouspour BIB018 developed a 2-D boundary extraction method based on a probabilistic data association filter. They posed the 2-D segmentation problem as an estimation of a moving object along the cavity boundary, where the motion is governed by a finite set of dynamical models subject to uncertainty. Hence, the motion model along the contour models the prior smoothness of the contour. Angular discretisation of the contour from a manually selected seed point inside the cavity was used to estimate the radial distance of the boundary points to the seed point. In order to increase robustness to noise and accuracy, they combined multiple trajectory models, with different data noise levels and trajectory smoothness/dynamics. They used a probabilistic data association filter (PDAF) for boundary extraction and the interacting multiple model estimator (IMM) for model combination. 4 The boundary extraction was performed recursively using a modified Kalman filter with an approximately constant angular velocity model and where the candidate edge points were estimated using a 1-D derivative operator along each radial direction. A mathematical analysis of the PDAF filter with respect to the process and noise parameters was provided to help model selection based on the noise level in the image and the shape of the object boundary. Segmentation examples on several ultrasound applications (heart, breast, prostate) demonstrated the potential of this approach. Some limitations and open issues were highlighted mainly due to the angular discretisation and implicitly, the performance sensitivity to the choice of the seed point. The algorithm was fast and could be implemented in real time. However, the 3-D extension of the approach is not obvious. The model combination is particularly interesting. It can be viewed as an adaptive regularization of the boundaries because the models have different noise/smoothness tradeoffs. The IMM selects the dominant model where there is a disagreement and takes some kind of an average solution where the models agree. However, the selection of the edge candidates is based on intensity gradient and hence ignores the nature of the data. This was improved in BIB019 by using the Sticks algorithm BIB007 for edge enhancement as a pre-processing step before applying the PDAF/IMM algorithm. Yu et al. BIB020 presented a two-step semi-automatic activecontour based segmentation method. First, an initial elliptical approximation of the contour was obtained using two manually selected points. A rough binary segmentation was then obtained by optimizing an area-weighted mean-difference criterion in a level sets framework. Finally, a finer segmentation was accomplished via a parametric active contour model in a polar coordinate system (see also BIB010 for a polar coordinate active contour). In the second stage, a speckle reducing anisotropic diffusion, (SRAD)  , was applied to enhance the images and the instantaneous coefficient of variation (ICOV) was utilized as an edge cue in the derivation of the external energy of the active contour model. Note that log-compressed -scan images have to be decompressed before applying the SRAD algorithm. It is reported in  and BIB021 that the ICOV allows for balanced and well-localized edge strength measurements in bright as well as in dark regions of speckle images. In summary, the first stage of the segmentation algorithm uses global image statistics and the second one uses local image features to refine the segmentation. It is unclear why the authors used two steps since both energy functions could be minimized in the level sets framework (see, e.g., BIB015 ). A preliminary evaluation of the approach was presented on 27 selected images acquired from six patients. Manual delineations were performed by three sonographers. Only images with a "reasonable" interobserver variability were used. The computer generated boundaries agreed with the average manual contours within 2 mm. 76% of the results fell within one standard deviation of the manual contours.