Ultrasound image segmentation: a survey <s> F. Obstetrics and Gynecology <s> A multiresolution texture segmentation (MTS) approach to image segmentation that addresses the issues of texture characterization, image resolution, and time to complete the segmentation is presented. The approach generalizes the conventional simulated annealing method to a multiresolution framework and minimizes an energy function that is dependent on the resolution of the size of the texture blocks in an image. A rigorous experimental procedure is also proposed to demonstrate the advantages of the proposed MTS approach on the accuracy of the segmentation, the efficiency of the algorithm, and the use of varying features at different resolution. Semireal images, created by sampling a series of diagnostic ultrasound images of an ovary in vitro, were tested to produce statistical measures on the performance of the approach. The ultrasound images themselves were then segmented to determine if the approach can achieve accurate results for the intended ultrasound application. Experimental results suggest that the MTS approach converges faster and produces better segmentation results than the single-level approach. > <s> BIB001 </s> Ultrasound image segmentation: a survey <s> F. Obstetrics and Gynecology <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> F. Obstetrics and Gynecology <s> Rationale and Objectives. We designed an image processing technique to automatically measure the biparietal diameter (BPD) and head circumference (HC) from prenatal sonograms. We evaluated the performance of the algorithm by comparing the resulting measurements with those made by experienced sonographers. Methods. Thirty-five digitized sonograms of the fetal head were obtained during routine imaging. The BPD and HC were automatically computed by detecting the inner and outer boundaries of the fetal skull using the computer vision technique known as the “active contour model.” Six experienced sonographers also measured the BPD and HC on these images. Results. The algorithm failed to locate the boundaries in two of the 35 cases. For the remaining cases, the mean absolute difference between the automated measurements and the average of the six observers was 1.4% for BPD and 2.9% for HC. The correlations were .999 for the BPD and .994 for the HC. The computer's measurements were no different from the six observers' measurements than the observers' measurements were from one another. Conclusion. The tested algorithm effectively and accurately measures BPD and HC automatically. We are currently in the process of integrating this algorithm into an ultrasound machine. <s> BIB003 </s> Ultrasound image segmentation: a survey <s> F. Obstetrics and Gynecology <s> In this article, a model for the amplitude statistics of the backscattered ultrasonic signal is presented. We propose to view a tissue as being composed of a large number of small units, each having slightly different characteristics. This variability within the tissue is expressed by fluctuations in the parameters of the local probability distribution function (PDF). Based on analogous expressions derived for radio propagation and optical scintillations, the local PDF is modulated by a lognormal distribution of the local standard deviation. Integrating the local contributions yields the amplitude PDF for the entire tissue. We show that the local entropy is a normal variable, since for four different local PDFs it is linearly related to the logarithm of the local standard deviation. When the local entropy histogram exhibits distinct and multiple peaks, the local entropy distribution can be used for region segmentation. This fact is demonstrated for ultrasonic images of ovarian cysts. <s> BIB004 </s> Ultrasound image segmentation: a survey <s> F. Obstetrics and Gynecology <s> Segmentation is often an important step in medical image analysis. The local entropy is a possible variable for segmenting ultrasound images containing fluid surrounded by a soft tissue. A commonly used tool for image segmentation is thresholding. Recently, a new thresholding technique, known as "minimum cross entropy thresholding" (MCE), has been proposed. We present a multivariate extension of MCE in which the segmented variable (gray level) is replaced by a weighted combination of several image parameters. We propose to use a bivariate extension of MCE, which uses a linear combination of the gray level and the local entropy. The results obtained are demonstrated for ultrasound images of ovarian cysts. <s> BIB005 </s> Ultrasound image segmentation: a survey <s> F. Obstetrics and Gynecology <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> BIB006 </s> Ultrasound image segmentation: a survey <s> F. Obstetrics and Gynecology <s> Abstract We have developed a tool to automatically detect inner and outer skull boundaries of a fetal head in ultrasound images. These boundaries are used to measure biparietal diameter (BPD) and head circumference (HC). The algorithm is based on active contour models and takes 32 s on a Sun SparcStation 20 71 . A high-performance desktop multimedia system called MediaStation 5000 (MS5000) is used as a model for our future ultrasound subsystem. On the MS5000, the optimized implementation of this algorithm takes 248 ms. The difference (between the computer-measured values on MS5000 and the gold standard) for BPD and HC was 1.43% (σ = 1.00%) and 1.96% (σ = 1.96%), respectively. According to our data analysis, no significant differences exist in the BPD and HC measurements made on the MS5000 and those measurements made on the Sun SparcStation 20 71 . Reduction in the overall execution time from 32 s to 248 ms will help making this algorithm a practical ultrasound tool for sonographers. <s> BIB007 </s> Ultrasound image segmentation: a survey <s> F. Obstetrics and Gynecology <s> The application of a knowledge-based segmentation method to the problem of automatically detecting the outer follicle wall boundary in ultrasonographic images of ovaries is presented. A combination of computer detection and interactive adjustment was used to define an approximate inner follicle-wall boundary, which was then used by the computer algorithm as a priori knowledge to automatically find the outer follicle-wall border. The segmentation algorithm was tested on ultrasonographic images of women's ovaries that were imaged in vivo. The semiautomatic segmentations were compared to segmentations by an expert human observer in terms of border placement differences and in terms of quantitative parameters relevant to the physiologic status of the follicles. These physiological parameters include total and specific signal intensity from the follicle and from the follicle wall. The computer-detected outer follicle wall boundaries correlated well with the human observer-defined wall boundaries, in terms of enclosed follicle area, specific and total follicle signal, enclosed wall area, and specific and total wall signal. The actual border placement differences were also small, with a maximum placement difference of 1.47 +/- 0.83 mm and a root mean square (r.m.s.) placement difference of 0.59 +/- 0.28 mm. <s> BIB008 </s> Ultrasound image segmentation: a survey <s> F. Obstetrics and Gynecology <s> Ovarian ultrasound is an effective tool in infertility treatment. Repeated measurements of the size and shape of follicles over several days are the primary means of evaluation by physicians. Currently, follicle wall segmentation is achieved by manual tracing which is time consuming and susceptible to inter-operator variation. An automated method for follicle wall segmentation is reported that uses a four-step process based on watershed segmentation and knowledge-based graph search algorithm which utilizes priori information about follicle structure for inner and outer wall detection. The automated technique was tested on 36 ultrasonographic images of women's ovaries. Validation against manually traced borders has shown good correlation of manually defined and computer-determined area measurements (R2 = 0.85 - 0.96). The border positioning errors were small: 0.63+/-0.36 mm for inner border and 0.67+/-0.41 mm for outer border detection. The use of watershed segmentation and graph search methods facilitates fast, accurate inner and outer border detection with minimal user-interaction. <s> BIB009 </s> Ultrasound image segmentation: a survey <s> F. Obstetrics and Gynecology <s> Knowledge about the status of the female reproductive system is important for fertility problems and age-related family planning. The volume of these fertility requests in our emancipated society is steadily increasing. Intravaginal 3D ultrasound imaging of the follicles in the ovary gives important information about the ovarian aging, i.e. number of follicles, size, position and response to hormonal stimulation. Manual analysis of the many follicles is laborious and error-prone. We present a multiscale analysis to automatically detect and quantify the number and shape of the patient's follicles. Robust estimation of the centres of the follicles in the speckled echographic images is done by calculating so-called winding number of the intensity singularity, i.e. the path integral of the angular increment of the direction of the gradient vector over a closed neighbourhood around the point. The principal edges on 200-500 intensity traces radiating from the detected singularity points are calculated by a multiscale edge focussing technique on 1D winding numbers. They are fitted with 3D spherical harmonic functions, from which the volume and shape parameters are derived. <s> BIB010 </s> Ultrasound image segmentation: a survey <s> F. Obstetrics and Gynecology <s> OBJECTIVE ::: To present the variable appearance of micrognathia in fetuses by three-dimensional ultrasonography and to describe practical methods for analysis of these volume data. ::: ::: ::: METHODS ::: Three-dimensional multiplanar imaging and surface-rendering techniques were used to show various syndromes and diagnostic approaches for the evaluation of fetal micrognathia. ::: ::: ::: RESULTS ::: Nine cases of fetal micrognathia are presented. Orthogonal multiplanar views were used to obtain a midsagittal facial profile. Examples of micrognathia include 3 cases of Pierre Robin sequence, cerebrocostomandibular syndrome, Cornelia de Lange syndrome, and hypochondrogenesis. Diagnostic pitfalls for micrognathia are also shown. ::: ::: ::: CONCLUSIONS ::: Three-dimensional multiplanar imaging increases the likelihood that a true midline sagittal view of the facial profile is being analyzed. Surface rendering provides another way to qualitatively evaluate the fetal chin from different viewing perspectives. Three-dimensional ultrasonographic methods are useful adjuncts to the preliminary diagnostic impression from two-dimensional ultrasonography. <s> BIB011 </s> Ultrasound image segmentation: a survey <s> F. Obstetrics and Gynecology <s> Abstract An improved algorithm is presented for ovarian follicle detection in ultrasound images. This fully automated recognition algorithm is composed of three successive steps. First, initial homogeneous regions are determined. Then, these initial regions are grown. The growing is controlled by average grey-level and by a newly introduced weighted image gradient. In the last stage, those regions are extracted that probably correspond to the follicles. The algorithm has been tested on 50 ovarian ultrasound images. The recognition rate of follicles using this procedure was around 78%. A possible extension of the algorithm deals with the entire information in the ultrasound image sequence, which is covered in Part II of this paper. <s> BIB012 </s> Ultrasound image segmentation: a survey <s> F. Obstetrics and Gynecology <s> Ovarian masses are a common phenomenon among women of all ages. The importance of prompt diagnosis of ovarian malignancies is obvious, due to the high mortality rate and the difficulty to detect a tumor in its early stages. In this work, an automatic technique for quantitative analysis and malignancy detection of ovarian masses using B-scan ultrasound (US) images is presented. The core of the technique is morphologic analysis of the ovarian mass. The method employed for this task is divided into two major stages: initial classification of the mass (into one of the three major tumor types: cyst, semisolid, solid), and detailed analysis of the mass. Malignancy evaluation is performed based on the collected data and the criteria provided by commonly used scoring systems. The results reflect adequate performance of the automatic method developed (referring to clinical requirements). <s> BIB013 </s> Ultrasound image segmentation: a survey <s> F. Obstetrics and Gynecology <s> This paper describes a new method for segmentation of fetal anatomic structures from echographic images. More specifically, we estimate and measure the contours of the femur and of cranial cross-sections of fetal bodies, which can thus be automatically measured. Contour estimation is formulated as a statistical estimation problem, where both the contour and the observation model parameters are unknown. The observation model (or likelihood function) relates, in probabilistic terms, the observed image with the underlying contour. This likelihood function is derived from a region-based statistical image model. The contour and the observation model parameters are estimated according to the maximum likelihood (ML) criterion, via deterministic iterative algorithms. Experiments reported in the paper, using synthetic and real images, testify for the adequacy and good performance of the proposed approach. <s> BIB014
The noninvasive nature of ultrasound is a strong argument for its use in obstetrics and gynecology. In obstetrics, segmentation provides valuable measurements in order to assess the growth of the foetus and in diagnosis of fetal malformation. Most analysis is based on 2-D scans. Standard measurements include the biparietal diameter, head circumference, length of the fetal femur, the abdominal circumference, and the amniotic fluid volume (see, e.g., BIB006 and BIB007 BIB014 BIB003 ). There is often a sharp contrast between the face of a foetus and the surrounding amniotic fluid, allowing automatic boundary detection. Hence, obstetrics is a potential field of application for volume rendering and visualization (see, e.g., BIB011 and  ). In gynecology, most of reported segmentation algorithms address ovarian follicle measurement and cyst detection and measurement  . Muzzolini et al. BIB001 used a split-and merge segmentation approach to segment 2-D ultrasound images of ovarian follicles. The split and merge operations were controlled by means of a simulated annealing algorithm (Metropolis). The authors used a texture-based measure for block comparison in the splitting and merging process which takes into account the block size. In a later work, the same team proposed a robust texture feature selection method based on outlier rejection to be used in the segmentation algorithm  . A semi-automatic method for ovarian follicles segmentation was reported by Sarty et al. BIB008 . The technique is a knowledge based approach and is similar to earlier work of the team BIB002 . It simultaneously detects the inner and the outer border of the follicle of interest, which was interactively selected by defining an angular region of interest. Both border detections were based on the minimization of a cost function using heuristic graph searching techniques. Polar coordinates were used and edges strength and direction were considered in the cost function definition which incorporates some prior knowledge. The outer wall was automatically detected after the detection of the inner wall, for which frequent manual correction were required. Measurements based upon the algorithm's outer border correlated well with those based on a human expert manual boundary tracing (31 ultrasound images were used). A similar work was reported by Krivanek and Sonka for follicle segmentation BIB009 . An automatic approximation of the inner follicles wall using watershed segmentation on a smoothed image was first performed. Then the knowledge graph searching method was applied to detect both walls of the follicle of interest. Potočnik and Zazula proposed a three step algorithm for automatic segmentation of follicles in 2-D ultrasound images BIB012 . The first step automatically detected seed points based on a combination of a watershed segmentation and several thresholding operations on prefiltered images. In the second step, accurate boundary detection of the follicles was performed using a region growing segmentation starting from the previously detected seed points. The pixel aggregation was controlled using a criterion based on the mean gray level values and a weighted gradient. The final step was a recognition step which used prior knowledge about the follicles in order to eliminate nonfollicle detected regions (shape, size, and localization within an image). The authors extended this work to the segmentation of a sequence of images  . The new algorithm used the detected objects in the previous frame in order to predict the object in the next frame by means of a Kalman filter. The algorithm was tested on 50 cases with a recognition rate around 78% and a boundary accuracy around 1.1 mm (mean error in comparison to manual boundary tracing). The results are encouraging probably due mostly to the appropriate prior knowledge used in the algorithm. 6 Zimmer et al. BIB004 made an interesting observation, showing that the local gray level entropy is a normal variable, under the assumption that the local gray level pdf follows a Gaussian, Rayleigh, Weibull, or Nakagami distribution. This suggests that the local entropy may be successfully used for region segmentation when its histogram shows multiple distinct peaks. Indeed, the same team presented a bivariate extension of an entropy-based thresholding method, the "minimum cross entropy thresholding," in which the segmented variable is replaced by a linear combination of the gray level and the local entropy BIB005 . The binary segmentation algorithm was demonstrated on ultrasound images with ovarian cysts in BIB005 and was used in the segmentation step in a recent work for quantitative analysis and malignancy detection of ovarian masses BIB013 . The demonstration of the normality of the local entropy under more complete models, like the generalized -distribution, is still to be done (see Section IV-B). All of the above methods operate in 2-D which mimics clinical practice. However, one is really looking at a 3-D object, and the interest is to measure volume rather than a linear dimension (Euclidean distance). Romeny et al. BIB010 described a method for segmenting follicles from 3-D ultrasound data which was fully automatic and consisted of two steps (follicles centers detection followed by 1-D edge detection along radial rays generated from the follicle's center). The centers detection method was based on the so-called winding number of the intensity sin- gularity. 7 The edge points along the 1-D profiles were detected using a robust multiscale zero-crossing of the second derivative. The lifetime of an edge pattern over scale was used as a measure of its significance. Gooding et al.  presented a level set based method for segmenting sparse freehand ultrasound data of the ovary where the speed function is a weighted sum of three terms, a surface reconstruction term, a regularization term and an image term. The image term was region-based and defined in terms of nonparametric probabilities of labeled regions. The method required a good initialization. The method is more extensively discussed 7 The winding number is defined as the number of times the image gradient vector rotates over 2 when a closed curve is traversed about a fixed point, i.e., the path integral of the angular increment of the direction of the gradient vector over a closed neighborhood around a point. in  including a comparison of volume estimates with aspirated volumes. Tables I-IV summarize Axis, XC apical X Chamber ( , 3, 4), TEE transoesoephageal.