Ultrasound image segmentation: a survey <s> E. IVUS (Vascular Disease) <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> E. IVUS (Vascular Disease) <s> Intravascular ultrasound imaging of coronary arteries provides important information about coronary lumen, wall, and plaque characteristics. Quantitative studies of coronary atherosclerosis using intravascular ultrasound and manual identification of wall and plaque borders are limited by the need for observers with substantial experience and the tedious nature of manual border detection. We have developed a method for segmentation of intravascular ultrasound images that identifies the internal and external elastic laminae and the plaque-lumen interface. The border detection algorithm was evaluated in a set of 38 intravascular ultrasound images acquired from fresh cadaveric hearts using a 30 MHz imaging catheter. To assess the performance of our border detection method we compared five quantitative measures of arterial anatomy derived from computer-detected borders with measures derived from borders manually defined by expert observers. Computer-detected and observer-defined lumen areas correlated very well (r=0.96, y=1.02x+0.52), as did plaque areas (r=0.95, y=1.07x-0.48), and percent area stenosis (r=0.93, y=0.99x-1.34.) Computer-derived segmental plaque thickness measurements were highly accurate. Our knowledge-based intravascular ultrasound segmentation method shows substantial promise for the quantitative analysis of in vivo intravascular ultrasound image data. <s> BIB002 </s> Ultrasound image segmentation: a survey <s> E. IVUS (Vascular Disease) <s> Abstract One prerequisite for standard clinical use of intravascular ultrasound imaging is rapid evaluation of the data. The main quantities to be extracted from the data are the size and the shape of the lumen. Until now, no accurate, robust and reproducible method to obtain the lumen boundaries from intravascular ultrasound images has been described. In this study, 21 different (semi-)automated binary-segmentation methods for determining the lumen are compared with manual segmentation to find an alternative for the laborious and subjective procedure of manual editing. After a preprocessing step in which the catheter area is filled with lumen-like grey values, all approaches consist of two steps: (i) smoothing the images with different filtering methods and (ii) extracting the lumen by an object definition method. The combination of different filtering methods and object definition methods results in a total of 21 methods and 80 experiments. The results are compared with a reference image, obtained from manual editing, by use of four different quality parameters—two based on squared distances and two based on Mahalanobis distances. The evaluation has been carried out on 15 images, of which seven are obtained before balloon dilation and eight after balloon dilation. While for the post-dilation images no definite conclusions can be drawn, an automated contour model applied to images smoothed with a large kernel appears to be a good alternative to manual contouring. For pre-dilation images a fully automated active contour model, initialized by thresholding, preceded by filtering with a small-scale median filter is the best alternative for manual delineation. The results of this method are even better than manual segmentation, i.e. they are consistently closer to the reference image than the average distance of all individual manual segmentations. <s> BIB003 </s> Ultrasound image segmentation: a survey <s> E. IVUS (Vascular Disease) <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> BIB004 </s> Ultrasound image segmentation: a survey <s> E. IVUS (Vascular Disease) <s> Abstract Intravascular ultrasound (IVUS) provides exact anatomy of arteries, allowing accurate quantitative analysis. Automated segmentation of IVUS images is a prerequisite for routine quantitative analyses. We present a new three-dimensional (3D) segmentation technique, called active surface segmentation, which detects luminal and adventitial borders in IVUS pullback examinations of coronary arteries. The technique was validated against expert tracings by computing correlation coefficients (range 0.83–0.97) and William's index values (range 0.37–0.66). The technique was statistically accurate, robust to image artifacts, and capable of segmenting a large number of images rapidly. Active surface segmentation enabled geometrically accurate 3D reconstruction and visualization of coronary arteries and volumetric measurements. <s> BIB005 </s> Ultrasound image segmentation: a survey <s> E. IVUS (Vascular Disease) <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> BIB006 </s> Ultrasound image segmentation: a survey <s> E. IVUS (Vascular Disease) <s> Automated edge detection may standardize measurements among observers, providing for rapid assessment of intravascular ultrasound (IVUS) images. However, with high frequency images, enhanced blood signals make it difficult to define and trace the lumen borders. Accordingly, we evaluated a fully automated contour analysis facilitated with a blood noise reduction algorithm (BNR) for 40-MHz IVUS images in human coronary arteries of 27 patients. This algorithm is based on the principle that blood echo speckles have higher temporal and spatial variations than the arterial wall. A total of 193 paired lumen areas and 78 external elastic membrane (EEM) areas were measured and compared. Automated measurements showed good agreement with manual tracings for lumen and EEM area, with high correlation coefficients (0.945 and 0.950, respectively) and small variability (0.4 ± 14.4% and 0.6 ± 9.7%, respectively). This preliminary finding suggests that automated contour detection facilitated with BNR appeared to be a feasible and reliable technique for area measurements in 40-MHz IVUS imaging. <s> BIB007 </s> Ultrasound image segmentation: a survey <s> E. IVUS (Vascular Disease) <s> Intravascular ultrasound (IVUS) provides direct depiction of coronary artery anatomy, including plaque and vessel area, which is important in quantitative studies on the progression or regression of coronary artery disease. Traditionally, these studies have relied on manual evaluation, which is laborious, time consuming, and subject to large interobserver and intraobserver variability. A new technique, called active surface segmentation, alleviates these limitations and makes strides toward routine analyses. However, for three-dimensional (3-D) plaque assessment or 3-D reconstruction to become a clinical reality, methods must be developed which can analyze many images quickly. Presented is a comparison between two active surface techniques for three-dimensional segmentation of luminal and medial-adventitial borders. The force-acceleration technique and the neighborhood-search technique accurately detected both borders in vivo (r2 = 0.95 and 0.99, Williams' index = 0.67 and 0.65, and r2 = 0.95 and 0.99, WI = 0.67 and 0.70, respectively). However, the neighborhood-search technique was significantly faster and required less computation. Volume calculations for both techniques (r2 = 0.99 and r2 = 0.99) also agreed with a known-volume phantom. Active surface segmentation allows 3-D assessment of coronary morphology and further developments with this technology will provide clinical analysis tools. <s> BIB008 </s> Ultrasound image segmentation: a survey <s> E. IVUS (Vascular Disease) <s> Abstract Segmentation of intravascular ultrasound images provides important information about the degree of vessel obstruction as well as about the shape and size of plaques. To address the problems of inter- and intra-observer variances associated with conventional manual tracing, a fully automated segmentation was developed. The algorithm is based on the optimisation of a maximum a posteriori estimator, implementing the Rayleigh distribution of speckle and a priori information about the contours. Within 3D image sets, additional information by the blood flow resulting in a decorrelation of the pixels within the luminal boundary is used to initialise the segmentation. To accelerate the estimation, dynamic programming was used. The segmentation algorithm was realised as a Windows® 95 application on a Pentium® II/233 MHz and delivered reliable and reproducible results independent of the catheter position and the total image brightness (except overflow). In contrast, contours drawn by two physicians for an evaluation of 29 clinical cases showed large intra- and inter-observer variances. In vivo images were acquired with a 20 MHz transducer array (EndoSonics InVision TM ). Comparison with the contours drawn by the physicians and histology demonstrates the potential of the segmentation algorithm. <s> BIB009 </s> Ultrasound image segmentation: a survey <s> E. IVUS (Vascular Disease) <s> This paper presents a new general framework for image segmentation. A level set formulation is used to model the boundaries of the image regions and a new Multilabel Fast Marching is introduced for the evolution of the region contours toward the segmentation result. Statistical tests are performed to yield an initial estimate of high-confidence subsets of the image regions. Furthermore, the velocities for the propagation of the region contours are defined in accordance with the a posteriori probability of the respective regions, leading to the Bayesian Level Set methodology described in this paper. Typical segmentation problems are considered and experimental results are given to illustrate the robustness of the method against noise and its performance in precise region boundary localization. <s> BIB010 </s> Ultrasound image segmentation: a survey <s> E. IVUS (Vascular Disease) <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> BIB011 </s> Ultrasound image segmentation: a survey <s> E. IVUS (Vascular Disease) <s> In this paper, we report on a semiautomatic method for segmentation of three-dimensional (3D) carotid vascular ultrasound (US) images. Our method is based on a dynamic balloon model represented by a triangulated mesh. The mesh is manually placed within the interior of the carotid vessels, then is driven outward until it reaches the vessel wall by applying an inflation force to the mesh. Once the mesh is in close proximity to the vessel wall, it is further deformed using an image-based force, in order to better localize the boundary. Since the method requires manual initialization, there is inherent variability in the position and shape of the final segmented boundary. Using a 3D US image of a patient's carotids, we have examined the local variability in boundary position as the initialization position is varied throughout the interior of the carotid vessels in the 3D image. We have compared the semiautomatic segmentation method to a fully manual segmentation method, and found that the semiautomatic approach is less variable than the intraobserver variability for manual segmentation. We have furthermore examined the accuracy of the semiautomatic method by comparing the average surface to an "ideal" surface, determined by the average manually segmented surface. We have found, in general, good agreement between the semiautomatic and manual segmentation methods. For the 3D US image in question, the mean separation between the average segmented surface and the gold standard was found to be 0.35 mm. The two surfaces were determined to agree with each other, within uncertainty, at 65% of the mesh points comprising the two surfaces. <s> BIB012 </s> Ultrasound image segmentation: a survey <s> E. IVUS (Vascular Disease) <s> This paper is concerned with the use of the level set formalism to segment anatomical structures in 3D medical images (ultrasound or magnetic resonance images). A closed 3D surface propagates towards the desired boundaries through the iterative evolution of a 4D implicit function. The major contribution of this work is the design of a robust evolution model based on adaptive parameters depending on the data. First the iteration step and the external propagation force, both usually constant, are automatically computed at each iteration. Additionally, region-based information rather than the gradient is used, via an estimation of intensity probability density functions over the image. As a result, the method can be applied to various kinds of data. Quantitative and qualitative results on brain MR images and 3D echographies of carotid arteries are discussed. <s> BIB013 </s> Ultrasound image segmentation: a survey <s> E. IVUS (Vascular Disease) <s> We have developed an automated segmentation method for three-dimensional vascular ultrasound images. The method consists of two steps: an automated initial contour identification, followed by application of a geometrically deformable model (GDM). The formation of the initial contours requires the input of a single seed point by the user, and was shown to be insensitive to the placement of the seed within a structure. The GDM minimizes contour energy, providing a smoothed final result. It requires only three simple parameters, all with easily selectable values. The algorithm is fast, performing segmentation on a 336 x 352 x 200 volume in 25 s when running on a 100 MHz 9500 Power Macintosh prototype. The segmentation algorithm was tested on stenosed vessel phantoms with known geometry, and the segmentation of the cross-sectional areas was found to be within 3% of the true area. The algorithm was also applied to two sets of patient carotid images, one acquired with a mechanical scanner and the other with a freehand scanning system, with good results on both. <s> BIB014 </s> Ultrasound image segmentation: a survey <s> E. IVUS (Vascular Disease) <s> An aneurysm of the abdominal aorta (AAA) is characterized by modified wall properties, and a balloon-like area usually filled by a thrombus. A rupture of an aortic aneurysm can be fatal, yet there is no way to accurately predict such an occurrence. The study of the wall and thrombus cross-sectional distension, due to a pressure wave, is important as a way of assessing the degradation of the mechanical properties of the vessel wall and the risk of a rupture. Echo ultrasound transverse cross-sectional imaging is used here to study the thrombus and the aortic wall distension, requiring their segmentation within the image. Polar coordinates are defined, and a search is performed for minimizing a cost function, which includes a description of the boundary (based on a limited series of sine and cosine functions) and information from the image intensity gradients along the radii. The method is based on filtering by a modified Canny-Deriche edge detector and then on minimization of an energy function based on five parts. Since echoes from blood in the lumen and the thrombus produce similar patterns and speckle noise, a modified version for identifying the lumen-thrombus border was developed. The method has been validated by various ways, including parameter sensitivity testing and comparison to the performance of an expert. It is robust enough to track the lumen and total arterial cross-sectional area changes during the cardiac cycle. In 34 patients where sequences of images were acquired, the border between the thrombus and the arterial wall was detected with errors less than 2%, while the lumen-thrombus border was detected with a mean error of 4%. Thus, a noninvasive measurement of the AAA cross-sectional area is presented, which has been validated and found to be accurate. <s> BIB015 </s> Ultrasound image segmentation: a survey <s> E. IVUS (Vascular Disease) <s> Deformable template models and Markov chain Monte Carlo methods are used for analysing a space-time process of intracoronary ultrasound images in order to detect the artery contour and various other characteristics as a function of time. <s> BIB016 </s> Ultrasound image segmentation: a survey <s> E. IVUS (Vascular Disease) <s> In this work a new statistic deformable model for 3D segmentation of anatomical organs in medical images is proposed. A statistic discriminant snake performs a supervised learning of the object boundary in an image slice to segment the next slice of the image sequence. Each part of the object boundary is projected in a feature space generated by a bank of Gaussian filters. Then, clusters corresponding to different boundary pieces are constructed by means of linear discriminant analysis. Finally, a parametric classifier is generated from each contour in the image slice and embodied into the snake energy-minimization process to guide the snake deformation in the next image slice. The discriminant snake selects and classifies image features by the parametric classifier and deforms to minimize the dissimilarity between the learned and found image features. The new approach is of particular interest for segmenting 3D images with anisotropic spatial resolution, and for tracking temporal image sequences. In particular, several anatomical organs from different imaging modalities are segmented and the results compared to expert tracings. <s> BIB017 </s> Ultrasound image segmentation: a survey <s> E. IVUS (Vascular Disease) <s> The precise tomographic assessment of coronary artery disease by intravascular ultrasound (IVUS) is useful in quantitative studies. Such studies require identification of luminal and medial-adventitial (MA) borders in a sequence of IVUS images. We have developed a three-dimensional (3D) active-surface system for border detection that facilitates the analysis of many images with minimal user interaction. To assess the validity of the technique, luminal and MA borders in 529 end-diastolic images from nine coronary arterial segments (58.8 ± 14.2 images per patient) were traced manually by four experienced observers. The computer-detected borders were compared with borders determined by the four observers using a modified Williams' index (WI), the ratio of inter-observer variability to computer-observer variability. While manual tracing required 49.2 ± 12.1 min for analysis, the analysis system identified luminal (R2 = 0.92) and MA borders (R2 = 0.97) in 13.8 ± 4.0 min, a decrease of 35.4 min (p < 0.000001). The computer minus observer differences in lumen area and MA area were −0.88 ± 0.90 and −0.07 ± 0.63 mm2. Therefore, the computer system underestimated both lumen and MA area, but this effect was very small in MA area. The WI values and 95% confidence intervals were 0.98 (0.89,1.06) for luminal border detection and 0.99 (0.95,1.04) for MA border detection. Plaque volume measurements, a common endpoint of clinical trials, also verified the accuracy of the technique (R2 = 0.98). The proposed 3D active-surface border detection system provides a faster and less-tedious alternative to manual tracing for assessment of coronary artery anatomy in vivo. <s> BIB018 </s> Ultrasound image segmentation: a survey <s> E. IVUS (Vascular Disease) <s> In this paper, a fully automatic method for luminal contour segmentation in intracoronary ultrasound imaging is introduced. Its principle is based on a contour with a priori properties that evolves according to the statistics of the ultrasound texture brightness, which is generally Rayleigh distributed. The main interest of the technique is its fully automatic character. This is insured by an initial contour that is not set by the user, like in classical snake-based algorithms, but estimated and, thus, adapted to each image. Its estimation combines two pieces of information extracted from the a posteriori probability function of the contour position: the function maximum location (or maximum a posteriori estimator) and the first zero-crossing of its derivative. Then, starting from the initial contour, a region of interest is automatically selected and the process iterated until the contour evolution can be ignored. In vivo coronary images from 15 patients, acquired with the 20-MHz central frequency Jomed Invision ultrasound scanner, were segmented with the developed method. Automatic contours were compared to those manually drawn by two physicians in terms of mean absolute difference. The results demonstrate that the error between automatic contours and the average of manual ones is of small amplitude, and only very slightly higher (0.099 +/- 0.032 mm) than the interexpert error (0.097 +/- 0.027 mm). <s> BIB019 </s> Ultrasound image segmentation: a survey <s> E. IVUS (Vascular Disease) <s> Abstract A novel multi-agent image interpretation system has been developed which is markedly different from previous approaches in especially its elaborate high-level knowledge-based control over low-level image segmentation algorithms. Agents dynamically adapt segmentation algorithms based on knowledge about global constraints, contextual knowledge, local image information and personal beliefs. Generally agent control allows the underlying segmentation algorithms to be simpler and be applied to a wider range of problems with a higher reliability. The agent knowledge model is general and modular to support easy construction and addition of agents to any image processing task. Each agent in the system is further responsible for one type of high-level object and cooperates with other agents to come to a consistent overall image interpretation. Cooperation involves communicating hypotheses and resolving conflicts between the interpretations of individual agents. The system has been applied to IntraVascular UltraSound (IVUS) images which are segmented by five agents, specialized in lumen, vessel, calcified-plaque, shadow and sidebranch detection. IVUS image sequences from 7 patients were processed and vessel and lumen contours were detected fully automatically. These were compared with expert-corrected semiautomatically detected contours. Results show good correlations between agents and expert with r=0.84 for the lumen and r=0.92 for the vessel cross-sectional areas, respectively. <s> BIB020 </s> Ultrasound image segmentation: a survey <s> E. IVUS (Vascular Disease) <s> Abstract This paper describes an approach for fully automated segmentation of intravascular ultrasound (IVUS) images that mimics the procedure performed by human experts. The approach requires no manual initialization or interaction, and is integrated into our previously developed system for reconstruction of coronary arteries by data fusion of IVUS and X-ray angiography. The fusion system is used in an ongoing clinical study assessing the relationships among vessel curvature, plaque thickness, and hemodynamic shear stress. The improved automatic segmentation of IVUS images has resulted in a substantial decrease in the time needed for analysis, thus allowing for an increase in the size and scope of the clinical study. Preliminary results from 10 in vivo cases are presented. <s> BIB021 </s> Ultrasound image segmentation: a survey <s> E. IVUS (Vascular Disease) <s> Intravascular ultrasound (IVUS) produces images of arteries that show the lumen in addition to the layered structure of the vessel wall. A new automatic 3D IVUS fast-marching segmentation model is presented. The method is based on a combination of region and contour information, namely the gray level probability density functions (PDFs) of the vessel structures and the image gradient. Accurate results were obtained on in-vivo and simulated data with average point to point distances between detected vessel wall boundaries and validation contours below 0.105 mm. Moreover, Hausdorff distances (that represent the worst point to point variations) resulted in values below 0.344 mm, which demonstrate the potential of combining region and contour information in a fast-marching scheme for 3D automatic IVUS image processing. <s> BIB022
IVUS is increasingly used as a complementary tool to contrast X-ray angiography. It is a real-time, high resolution, and noninvasive imaging technique, which provides valuable anatomical information about the coronary arterial wall and plaque. Recent developments, include ECG-gated acquisition to overcome cyclic motion artifacts and IVUS elastography which provides biomechanical property estimates of vessel wall and artherosclerotic plaques, that are helpful in diagnosis and in guiding interventional procedures  ,  . Accurate segmentation of the lumen, the plaque, and the wall borders is a prerequisite for quantitative analysis. Most, if not all, of the IVUS segmentation literature, uses contour modeling and could be classified into one of three categories: edge driven BIB006 , BIB002 BIB007 BIB003 BIB005 BIB008 BIB018 , statistical  BIB022 BIB019 BIB009 BIB016 , and high-level knowledge driven BIB020 , BIB021 . We review some of the important papers in this section. Sonka et al. BIB002 presented a semi-automatic knowledge-based segmentation approach that identifies the internal and external lumina and the plaque-lumen boundaries. A novelty of the approach was the attempt to incorporate knowledge to mimic constraints used by an experienced ultrasonographer. The method used dynamic programming to solve a cost function based on edge strength and incorporated a priori knowledge about cross-sectional arterial anatomy (such as object shape, edge direction, double echo pattern, wall thickness). However, the method has several modeling limitations namely: the edge strength did not include speckle statistics and some prior information was included in a binary fashion with hard thresholds. A similar model was used by Takagi et al. BIB007 , and reported a similar performance. Their work differs from the work of Sonka et al. mainly in the preprocessing step. An adaptive spatio-temporal filtering of speckle was performed based on pre-segmentation of blood and tissue areas. Subtraction of two consecutive IVUS images in time acquired at the same position can increase the signal-to-noise ratio of the lumen area, i.e., obtain good contrast between the lumen and other parts in the image. Bouma et al. BIB003 compared several combinations of filtering techniques (Gaussian, median, anisotropic diffusion) followed by lumen detection (thresholding, region-growing, DDC  ). Experiments were carried out on 15 IVUS images, obtained using an average of 20 subtracted images all acquired at the same location. The evaluation against four experts showed that all the methods performed well on good quality data and less well on difficult cases. The automatic DDC, preceded with a small scale median filter was found to be the best alternative method to manual segmentation. An extension of the DDC model has been proposed for 3-D semi-automatic segmentation of the lumen and adventitial borders in serial IVUS images BIB005 . The same group presented a faster method, the fast active surface (FAS) BIB008 based on Williams and Shah's fast active contours (see Fig. 4(a) for an example result). The former method is a force-acceleration technique and the latter is a neighborhood-search method, and both snakes used only intensity gradient information. An editing tool was provided for user correction. In experiments, both techniques performed well although the FAS was 60 times faster. The FAS method was further validated on 529 ECG-gated IVUS images against four observers BIB018 using the measures defined in BIB004 . Kovalski et al. BIB006 used a simplified 3-D balloon model where the control points can move only along the radial direction, and used polar coordinates to represent the contours which removes one degree-of-freedom. The performance of the method was assessed on 88 images against two observers. The reported inter-observer area variability was in the same range as in BIB018 . However, the computer to manual area variability was slightly higher than the inter-observer one, for both the lumen and adventitial borders. In contrast to the method in BIB008 , the method of Kovalski et al. BIB006 was fully automatic. The above methods use simple derivative operators to estimate intensity gradients. Recently Pardo et al. BIB017 , proposed a statistical deformable model that was applied to IVUS images. They used a bank of Gaussian derivative filters, at different orientations and scales, to locally describe edge/nonedge cues, in an adaptive fashion (i.e., locally). The feature space was reduced by linear discriminant analysis, and a parametric classifier was employed to guide the model deformation by minimizing the dissimilarity between measured image features and the learned ones. The statistical learning approach made the algorithm more robust in comparison to using intensity gradient information, which supposes a prior model of the edge. 5 A Bayesian level set approach was proposed for a semi-automatic segmentation of the lumen, intima, and media borders using the shifted Rayleigh law as a model for the graylevel statistics  . This work is based on the previously published Bayesian level set method of Sifakis et al. BIB010 . Robustness to initialization was assessed, and a comparison made to an intensity gradient level-set method, intensity gradient based snakes BIB008 and PDF-snakes using the same model as in BIB011 . Fifteen IVUS images were used, acquired at 20 Mhz without ECG-gating, and with three different initializations. The PDF-snakes were more stable on average. However, the fast-marching method had the smallest Hausdorff distance. As expected, the gradient-snake had the largest variability to initialization as it does not model well the data. In a more recent work BIB022 , the same team proposed an automatic variant of their previous work (automatic estimation of the PDF region statistics parameters and the initial level set contours). In the new algorithm, the speed function was based on region statistics and gray level gradient information rather than region statistics alone. The algorithm was tested on 600 IVUS frames acquired at 20 MHz from a diseased superficial femoral artery and on a sequence of 86 simulated IVUS images. The Hausdorff distances were below 0.066 mm for the simulated data and below 0.344 mm for the in vivo data in comparison to one observer manual border delineation. A posteriori probability based segmentation using a Rayleigh distribution to model the data constraint and a 1-D Markov process to model the contour prior, was also used in BIB019 , BIB009 . Both methods use polar coordinates, are fully automatic and are related to previous work by Friedland and Adam BIB001 and Dias and Leitão  . Haas et al. BIB009 considered multiple contours and modeled the sequence as a first-order Markov process to account for the temporal continuity of the vessel walls. Brusseau et al. BIB019 considered only the segmentation of the luminal border (see Fig. 1(b) for two results taken from their paper). However, as the authors showed, the luminal border does not always correspond to the global maximum of the posterior pdf, nor to the first maximum, along radial direction. The algorithm first detected the "reliable" points that are the first or the global maximum along the radial direction and then choose either the global or the first maximum depending on the contour prior energy. Both methods were evaluated on several clinical cases against two experts, and good correlation was obtained. Hansen et al. BIB016 presented a fully Bayesian analysis of space time process of IVUS image based on deformable template models and Markov Chain Monte Carlo (MCMC) simulation. Unfortunately, they used a Gaussian model for both tissues and only applied their method to one sequence. Guérault et al.  presented a constrained maximum likelihood approach. The originality of their work was not in the data term, which uses a Rayleigh model, but in the modeling of the prior information about the geometric deformations inherent in IVUS images. Based on the hypothesis of a circular vessel, they constrained the search space using a parametric model of the contour which takes into account the catheter position and its orientation. A stochastic minimization procedure was utilized for the maximization of the criterion. Recently, two approaches have been published which aim to incorporate high-level knowledge in IVUS image segmentation. A machine learning approach mimicking the human visual system was proposed for automatic detection of the luminal and the medial-adventitial borders in BIB021 . The accuracy of the method was assessed on a reasonable data set. Bovenkamp et al. BIB020 , considered solving the segmentation problem using a multiagent knowledge approach. Its elaborate high-level knowledge (450 rules) takes control over simple low-level segmentation algorithm. A major drawback of IVUS is its inability to consider the vessel curvature and the orientation of the imaging catheter. Hence, quantifications performed on these data are inevitably distorted, since the vessel curvature remains unconsidered. Fusion between intravascular ultrasound and biplane angiography provides a solution for correct 3-D reconstruction of the IVUS data (see, e.g.,  and  ). This is out of the scope of this review. Nevertheless, we comment that the research in this field uses angiography information only in the reconstruction step of the 3-D vessels. This is a valuable source of information that could be used profitably in the 3-D segmentation step of the IVUS images. Finally, we note that some attention has also been given to developing methods related to the diagnosis and treatment of vascular disease using a standard -mode ultrasound transducer (i.e., not IVUS), including work on segmentation of carotid vessels BIB012 BIB014 BIB013 and abdominal aortic aneurysms BIB015 .