Ultrasound image segmentation: a survey <s>  <s> This paper presents a method for segmentation and tracking of cardiac structures in ultrasound image sequences. The developed algorithm is based on the active contour framework. This approach requires initial placement of the contour close to the desired position in the image, usually an object outline. Best contour shape and position are then calculated, assuming that at this configuration a global energy function, associated with a contour, attains its minimum. Active contours can be used for tracking by selecting a solution from a previous frame as an initial position in a present frame. Such an approach, however, fails for large displacements of the object of interest. This paper presents a technique that incorporates the information on pixel velocities (optical flow) into the estimate of initial contour to enable tracking of fast-moving objects. The algorithm was tested on several ultrasound image sequences, each covering one complete cardiac cycle. The contour successfully tracked boundaries of mitral valve leaflets, aortic root and endocardial borders of the left ventricle. The algorithm-generated outlines were compared against manual tracings by expert physicians. The automated method resulted in contours that were within the boundaries of intraobserver variability. <s> BIB001 </s> Ultrasound image segmentation: a survey <s>  <s> A basic goal in the analysis of echocardiographic images is to identify the locations of the endocardial borders. This is necessary in order to create three-dimensional reconstructions, and to derive precise quantitative parameters from the images. Endocardial borders can be identified manually; however, this is time-consuming, inconvenient, and liable to human subjectivity. For this reason there has been a great deal of interest in automatic border identification methods. In recent years numerous studies have been published, describing a wide variety of proposed border identification algorithms. In spite of the large number of published studies, however, the relative merits of the various methods remain unclear. In this paper the various analysis techniques are reviewed and discussed. The reasons for the lack of definitive conclusions regarding the utility of these methods are described. Finally, the basic methodological requirements necessary for empirical evaluation of border identification techniques are described. <s> BIB002 </s> Ultrasound image segmentation: a survey <s>  <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> BIB003 </s> Ultrasound image segmentation: a survey <s>  <s> We consider the problem of obtaining interval estimates of vessel areas from ultrasound images of cross sections through the carotid artery. Robust and automatic estimates of the cross sectional area is of medical interest and of help in diagnosing atherosclerosis, which is caused by plaque deposits in the carotid artery. We approach this problem by using a deformable template to model the blood vessel outline, and use recent developments in ultrasound science to model the likelihood. We demonstrate that by using an explicit model for the outline, we can easily adjust for an important feature in the data: strong edge reflections called specular reflection. The posterior is challenging to explore, and naive standard MCMC algorithms simply converge too slowly. To obtain an efficient MCMC algorithm we make extensive use of computational efficient Gaussian Markov random fields, and use various block sampling constructions that jointly update large parts of the model. <s> BIB004 </s> Ultrasound image segmentation: a survey <s>  <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> BIB005 </s> Ultrasound image segmentation: a survey <s>  <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> BIB006 </s> Ultrasound image segmentation: a survey <s>  <s> In echocardiography, the radio-frequency (RF) image is a rich source of information about the investigated tissues. Nevertheless, very few works are dedicated to boundary detection based on the RF image, as opposed to envelope image. In this paper, we investigate the feasibility and limitations of boundary detection in echocardiographic images based on the RF signal. We introduce two types of RF-derived parameters: spectral autoregressive parameters and velocity-based parameters, and we propose a discontinuity adaptive framework to perform the detection task. In classical echographic cardiac acquisitions, we show that it is possible to use the spectral contents for boundary detection, and that improvement can be expected with respect to traditional methods. Using the system approach, we study on simulations how the spectral contents can be used for boundary detection. We subsequently perform boundary detection in high frame rate simulated and in vivo cardiac sequences using the variance of velocity, obtaining very promising results. Our work opens the perspective of a RF-based framework for ultrasound cardiac image segmentation and tracking. <s> BIB007 </s> Ultrasound image segmentation: a survey <s>  <s> Ultrasonography is evolving rapidly with important recent advances in high-density transducer arrays, one-and-one-half-dimensional transducers, broad-band transducers, increased scanner bandwidth, and more sophisticated image-formation routines. Technical advances have clearly improved accuracy of image readings, heightened contrast and resolution, reduced noise, and reduced image slice thickness. It is within this fertile environment that very high-frequency ultrasound, harmonic imaging, and ultrasound contrast agents have emerged. Clinical applications of ultrasonography have also been extended to new fields, such as skeletal status assessment, which have long been considered beyond ultrasound's reach, and over the past 15 years, quantitative ultrasound bone densitometry has become an important part of the armamentarium for osteoporosis diagnosis. The state of these innovations, their contributions to diagnostic and monitoring capabilities, as well as the new applications they bring into reach will be discussed. We will explore several applications currently under development including ultrasound biomicroscopy (eye, skin, small animals), quantitative perfusion assessment, and pathology evaluation. Thus, ongoing research has not only significantly added to diagnostic ultrasound's existing capabilities, but also promises to further broaden the range of its clinical and biological applications. <s> BIB008 </s> Ultrasound image segmentation: a survey <s>  <s> Breast cancer is the most frequently diagnosed malignancy and the second leading cause of mortality in women . In the last decade, ultrasound along with digital mammography has come to be regarded as the gold standard for breast cancer diagnosis. Automatically detecting tumors and extracting lesion boundaries in ultrasound images is difficult due to their specular nature and the variance in shape and appearance of sonographic lesions. Past work on automated ultrasonic breast lesion segmentation has not addressed important issues such as shadowing artifacts or dealing with similar tumor like structures in the sonogram. Algorithms that claim to automatically classify ultrasonic breast lesions, rely on manual delineation of the tumor boundaries. In this paper, we present a novel technique to automatically find lesion margins in ultrasound images, by combining intensity and texture with empirical domain specific knowledge along with directional gradient and a deformable shape-based model. The images are first filtered to remove speckle noise and then contrast enhanced to emphasize the tumor regions. For the first time, a mathematical formulation of the empirical rules used by radiologists in detecting ultrasonic breast lesions, popularly known as the "Stavros Criteria" is presented in this paper. We have applied this formulation to automatically determine a seed point within the image. Probabilistic classification of image pixels based on intensity and texture is followed by region growing using the automatically determined seed point to obtain an initial segmentation of the lesion. Boundary points are found on the directional gradient of the image. Outliers are removed by a process of recursive refinement. These boundary points are then supplied as an initial estimate to a deformable model. Incorporating empirical domain specific knowledge along with low and high-level knowledge makes it possible to avoid shadowing artifacts and lowers the chance of confusing similar tumor like structures for the lesion. The system was validated on a database of breast sonograms for 42 patients. The average mean boundary error between manual and automated segmentation was 6.6 pixels and the normalized true positive area overlap was 75.1%. The algorithm was found to be robust to 1) variations in system parameters, 2) number of training samples used, and 3) the position of the seed point within the tumor. Running time for segmenting a single sonogram was 18 s on a 1.8-GHz Pentium machine. <s> BIB009 </s> Ultrasound image segmentation: a survey <s>  <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> BIB010 </s> Ultrasound image segmentation: a survey <s>  <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> BIB011
. Examples of echocardiographic image segmentation. Short axis images from (a) Dias and Leitão  and from (b) Mikic et al. BIB001 . (c) Long axis images from Bosch et al. BIB005 . In practice, one finds that most new segmentation methodology is reported by placing it in context of related literature for a chosen clinical application and the few ultrasound segmentation reviews have been reported from this clinical domain perspective, e.g., BIB002 BIB006  . To our knowledge, this is the first comprehensive review of ultrasound segmentation methodology in a broad-sense. This points to some common methodology issues across different clinical applications in terms of prior modeling. There are a larger number of papers which describe general segmentation methods which show application of a method to one or two ultrasound images or sequences without specific reference to ultrasound image formation or context. We have not included these as they are much more difficult to identify. We have also not considered the segmentation of ultrasound parametric images such as elasticity images, and functional ultrasound contrast image sequences. Finally, we recognize that in the future, more work is likely to be done in segmentation based on the RF signal but access is not widely available so we have not considered this here. See BIB007 and BIB004 for recent segmentation papers based on this. The outline of the paper is as follows. In Section II, we review principal works on ultrasound image segmentation classified by clinical application as most researchers are interested in a segmentation solution for a given clinical problem. This section focuses on application areas in which the majority of effort has been focussed, namely segmentation of echocardiography, breast ultrasound, transrectal ultrasound (TRUS), intravascular ultrasound (IVUS) images, and ultrasound images acquired in obstetrics and gynecology. Examples from representative methods of the first four are respectively shown in Figs. BIB008 BIB002 BIB006  . In Section III, we summarize performance assessment and clinical validation that has been done in these areas to-date. Section IV will be of particular interest to researchers working on new segmentation algorithm development. Here, we focus on reviewing collectively first papers which have been motivated by and incorporate knowledge of ultrasound physics (par- BIB009 . From left to right: original image, contrast-enhanced version, detected boundary points and the output of deformable model. Fig. 3 . Segmentation example of transverse ultrasound images of the prostate from Gong et al BIB010 ; the solid contours are the ground truth established by averaging five experts' manual outlinings and the dotted contour are the computer generated boundaries. BIB003 showing the initial contour and the final borders. (b) Two segmentation examples of the automatic algorithm by Brusseau et al. BIB011 . ticularly speckle models) and then papers that have developed segmentation solutions that used other prior information (intensity, shape, and temporal models). We conclude in Section V by listing ten papers which have made contributions specific to ultrasound segmentation in terms of novel ideas and/or validation and may be useful as an introduction to those new to the field.