Ultrasound image segmentation: a survey <s> D. Other Cancer Types <s> Abstract This article describes a novel approach to the semi-automatic segmentation of ultrasound images. Assisted segmentation is particularly attractive when processing many slices through a 3D data set, and even though fully automatic segmentation would be ideal, this is currently not feasible given the quality of ultrasound images. The algorithm developed in this article is based on the active contour paradigm, with several important modifications. The contour is attracted to boundaries described locally by statistical models: this allows for the fact that the definition of what constitutes a boundary may vary around the boundary’s length. The statistical models are trained on-the-fly by observing boundaries accepted by the operator. In this way, operator intervention in a particular slice is sensibly exploited to reduce the need for intervention in subsequent slices. The resulting algorithm provides fast, reliable and verifiable segmentation of in vivo ultrasound images. <s> BIB001 </s> Ultrasound image segmentation: a survey <s> D. Other Cancer Types <s> Due to the speckles and the ill-defined edges of the object of interest, the classic image-segmentation techniques are usually ineffective in segmenting ultrasound (US) images. In this paper, we present a new algorithm for segmenting general US images that is composed of two major techniques; namely, the early-vision model and the discrete-snake model. By simulating human early vision, the early-vision model can capture both grey-scale and textural edges while the speckle noise is suppressed. By performing deformation only on the peaks of the distance map, the discrete-snake model promises better noise immunity and more accurate convergence. Moreover, the constraint for most conventional snake models that the initial contour needs to be located very close to the actual boundary has been relaxed substantially. The performance of the proposed snake model has been shown to be comparable to manual delineation and superior to that of the gradient vector flow (GVF) snake model. <s> BIB002 </s> Ultrasound image segmentation: a survey <s> D. Other Cancer Types <s> The snake model is a widely-used approach to finding the boundary of the object of interest in an ultrasound image. However, due to the speckles, the weak edges and the tissue-related textures in an ultrasound image, conventional snake models usually cannot obtain the desired boundary satisfactorily. In this paper, we propose a new adaptive snake model for ultrasound image segmentation. The proposed snake model is composed of three major techniques, namely, the modified trimmed mean (MTM) filtering, ramp integration and adaptive weighting parameters. With the advantages of the mean and median filters, the MTM filter is employed to alleviate the speckle interference in the segmentation process. The weak edge enhancement by ramp integration attempts to capture the slowly varying edges, which are hard to capture by conventional snake models. The adaptive weighting parameter allows weighting of each energy term to change adaptively during the deformation process. The proposed snake model has been verified on the phantom and clinical ultrasound images. The experimental results showed that the proposed snake model achieves a reasonable performance with an initial contour placed 10 to 20 pixels away from the desired boundary. The mean minimal distances from the derived boundary to the desired boundary have been shown to be less than 3.5 (for CNR > or = 0.5) and 2.5 pixels, respectively, for the phantom and ultrasound images. <s> BIB003 </s> Ultrasound image segmentation: a survey <s> D. Other Cancer Types <s> Edge detection is an important, but difficult, step in quantitative ultrasound (US) image analysis. In this paper, we present a new textural approach for detecting a class of edges in US images; namely, the texture edges with a weak regional mean gray-level difference (RMGD) between adjacent regions. The proposed approach comprises a vision model-based texture edge detector using Gabor functions and a new texture-enhancement scheme. The experimental results on the synthetic edge images have shown that the performances of the four tested textural and nontextural edge detectors are about 20%-95% worse than that of the proposed approach. Moreover, the texture enhancement may improve the performance of the proposed texture edge detector by as much as 40%. The experiments on 20 clinical US images have shown that the proposed approach can find reasonable edges for real objects of interest with the performance of 0.4 +/- 0.08 in terms of the Pratt's figure. <s> BIB004 </s> Ultrasound image segmentation: a survey <s> D. Other Cancer Types <s> Abstract Most deformable models require the initial contour to be placed close to the boundary of the object of interest for boundary extraction of ultrasound (US) images, which is impractical in many clinical applications. To allow a distant initial contour, a new dual-snake model promising high penetrability through the interference of the noises is proposed in this paper. The proposed dual-snake model features a new far-reaching external force, called the discrete gradient flow, a connected component-weighted image force, and an effective stability evaluation of two underlying snakes. The experimental results show that, with a distant initial contour, the mean distance from the derived boundary to the desired boundary is less than 1.4 pixels, and most snake elements are within 2.7 pixels of the desired boundaries for the synthetic images with CNR ≥ 1. For the clinical US images, the mean distance is less than 1.9 pixels, and most snake elements are within 3 pixels of the desired boundaries. (E-mail: chung@lotus.mc.ntu.edu.tw) <s> BIB005 </s> Ultrasound image segmentation: a survey <s> D. Other Cancer Types <s> A hybrid neural network is presented for the segmentation of ultrasound images.Feature vectors are formed by the discrete cosine transform of pixel intensities in region of interest (ROI). The elements and the dimension of the feature vectors are determined by considering only two parameters: The amount of ignored coefficients, and the dimension of the ROI.First-layer-nodes of the proposed hybrid network represent hyperspheres (HSs) in the feature space. Feature space is partitioned by intersecting these HSs to represent the distribution of classes. The locations and radii of the HSs are found by the genetic algorithms.Restricted Coulomb energy (RCE) network, modified RCE network, multi-layer perceptron and the proposed hybrid neural network are examined comparatively for the segmentation of ultrasound images. <s> BIB006 </s> Ultrasound image segmentation: a survey <s> D. Other Cancer Types <s> Two common deficiencies of most conventional deformable models are the need to place the initial contour very close to the desired boundary and the incapability of capturing a highly winding boundary for sonographic boundary extraction. To remedy these two deficiencies, a new deformable model (namely, the cell-based dual snake model) is proposed in this paper. The basic idea is to apply the dual snake model in the cell-based deformation manner. While the dual snake model provides an effective mechanism allowing a distant initial contour, the cell-based deformation makes it possible to catch the winding characteristics of the desired boundary. The performance of the proposed cell-based dual snake model has been evaluated on synthetic images with simulated speckles and on the clinical ultrasound (US) images. The experimental results show that the mean distances from the derived to the desired boundary points are 0.9 +/- 0.42 pixels and 1.29 +/- 0.39 pixels for the synthetic and the clinical US images, respectively. <s> BIB007 </s> Ultrasound image segmentation: a survey <s> D. Other Cancer Types <s> Support Vector Machines (SVMs) are a general algorithm based on guaranteed risk bounds of statistical learning theory. They have found numerous applications, such as in classification of brain PET images, optical character recognition, object detection, face verification, text categorization and so on. In this paper we propose the use of SVMs to segment lesions in ultrasound images and we assess thoroughly their lesion detection ability. We demonstrate that trained SVMs with a radial basis function kernel segment satisfactorily (unseen) ultrasound B-mode images as well as clinical ultrasonic images. <s> BIB008 </s> Ultrasound image segmentation: a survey <s> D. Other Cancer Types <s> This work presents a novel texture and shape priors based method for kidney segmentation in ultrasound (US) images. Texture features are extracted by applying a bank of Gabor filters on test images through a two-sided convolution strategy. The texture model is constructed via estimating the parameters of a set of mixtures of half-planed Gaussians using the expectation-maximization method. Through this texture model, the texture similarities of areas around the segmenting curve are measured in the inside and outside regions, respectively. We also present an iterative segmentation framework to combine the texture measures into the parametric shape model proposed by Leventon and Faugeras. Segmentation is implemented by calculating the parameters of the shape model to minimize a novel energy function. The goal of this energy function is to partition the test image into two regions, the inside one with high texture similarity and low texture variance, and the outside one with high texture variance. The effectiveness of this method is demonstrated through experimental results on both natural images and US data compared with other image segmentation methods and manual segmentation. <s> BIB009 </s> Ultrasound image segmentation: a survey <s> D. Other Cancer Types <s> In this paper, a novel method for the boundary detection of human kidneys from three dimensional (3D) ultrasound (US) is proposed. The inherent difficulty of interpretation of such images, even by a trained expert, makes the problem unsuitable for classical methods. The method here proposed finds the kidney contours in each slice. It is a probabilistic Bayesian method. The prior defines a Markov field of deformations and imposes the restriction of contour smoothness. The likelihood function imposes a probabilistic behavior to the data, conditioned to the contour position. This second function, which is also Markov, uses an empirical model of distribution of the echographical data and a function of the gradient of the data. The model finally includes, as a volumetric extension of the prior, a term that forces smoothness along the depth coordinate. The experiments that have been carried out on echographies from real patients validate the model here proposed. A sensitivity analysis of the model parameters has also been carried out. <s> BIB010 </s> Ultrasound image segmentation: a survey <s> D. Other Cancer Types <s> The feasibility of selecting fractal feature vector based on multiresolution analysis to segment suspicious abnormal regions of ultrasonic liver images is described in this paper. The proposed feature extraction algorithm is based on the spatial-frequency decomposition and fractal geometry. Segmentation of various liver diseases reveals that the fractal feature vector based on multiresolution analysis is trustworthy. A quantitative characterization based on the proposed unsupervised segmentation algorithm can be utilized to establish an automatic computer-aided diagnostic system. As well, to increase the visual interpretation capability of ultrasonic liver image for junior physicians, off-line learning software is developed to investigate the visual criteria. <s> BIB011
The previous two subsections have considered the two main areas of application of ultrasound in diagnosis of cancer. As ultrasound is increasingly used in the planning and treatment of interventions and therapy, it is worth mentioning that there are a growing number of publications related to (nonbreast and prostate) cancer diagnosis and therapy. Recent papers include those looking at ultrasound segmentation of the kidney and gallbladder BIB009 , BIB001 BIB010 BIB006 and the liver BIB008 BIB002 BIB003 BIB004 BIB005 BIB007 BIB011 .