Cell-based dual snake model: a new approach to extracting highly winding boundaries in the ultrasound images

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.     

An adaptive snake model for ultrasound image segmentation: modified trimmed mean filter, ramp integration and adaptive weighting parameters

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.     

An early vision-based snake model for ultrasound image segmentation

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.     

3-D breast ultrasound segmentation using active contour model

In this study, we made use of the discrete active contour model to overcome the natural properties of ultrasound (US) images, speckle, noise and tissue-related textures, to segment the breast tumors precisely. Determination of the real tumor boundary with the snake-deformation process requires an initial contour estimate. However, the manual way to sketch an initial contour is very time-consuming. Thus, we propose an automatic initial contour-finding method that not only maintains the tumor shape, but also is close to the tumor boundary and inside the tumor. During the deformation process, to prevent the snake trapping into the false position caused by tissue-related texture or speckle, we added the edge information as an image feature to define the external force. In addition, because the 3-D volume of a tumor is essentially constructed by a sequence of 2-D images, our method for finding boundaries of a tumor can be extended to 3-D cases. By precisely counting the volume of the 3-D images, we can get the volume of tumor. Finally, we will show that the proposed techniques have rather good performance and lead to a satisfactory result in comparison with the estimated volume and physician's estimate.     

一种半自动分割脑部MRI中胼胝体的方法

针对胼胝体的图像特点以及实际应用要求,采用半自动方法对MRI中的胼胝体进行分割。首先采用基于Live-Wire的算法对胼胝体影像的起始层和终止层进行初始分割,然后利用基于距离变换的形状插值算法获取中间层的初始轮廓信息,对插值获得的初始轮廓采用Snake模型进行局部收缩,获得真实的胼胝体边界。对序列MRI脑影像中的胼胝体进行分割、重建、标定。实验结果与临床医师的使用反馈证明,本文提出的算法具有较高的灵活性与可信度,对胼胝体的分割精度与解剖统计信息相符,分割结果可满足临床需求。     

Dynamic programming in parallel boundary detection with application to ultrasound intima-media segmentation

Segmentation of carotid artery intima-media in longitudinal ultrasound images for measuring its thickness to predict cardiovascular diseases can be simplified as detecting two nearly parallel boundaries within a certain distance range, when plaque with irregular shapes is not considered. In this paper, we improve the implementation of two dynamic programming (DP) based approaches to parallel boundary detection, dual dynamic programming (DDP) and piecewise linear dual dynamic programming (PL-DDP). Then, a novel DP based approach, dual line detection (DLD), which translates the original 2-D curve position to a 4-D parameter space representing two line segments in a local image segment, is proposed to solve the problem while maintaining efficiency and rotation invariance. To apply the DLD to ultrasound intima-media segmentation, it is imbedded in a framework that employs an edge map obtained from multiplication of the responses of two edge detectors with different scales and a coupled snake model that simultaneously deforms the two contours for maintaining parallelism. The experimental results on synthetic images and carotid arteries of clinical ultrasound images indicate improved performance of the proposed DLD compared to DDP and PL-DDP, with respect to accuracy and efficiency.     

Fetal abdominal contour extraction and measurement in ultrasound images

A novel method is developed for the fetal abdominal contour extraction and measurement in ultrasound images. Fetal abdominal circumference (AC) is one of the standardized measurements in the antepartum ultrasound monitoring. Among several standardized measurements, AC is best correlated with fetal growth but is also the most difficult to be accurately measured. To overcome the difficulties in the abdominal contour extraction, the proposed method is a four-step procedure that integrates several image segmentation techniques. The proposed method is able to make the best use of the strength of different segmentation algorithms, while avoiding their deficiencies. An enhanced instantaneous coefficient of variation (ICOV) edge detector is first developed to detect edges of the abdominal contour and alleviate the effects of most speckle noise. Then, the Fuzzy C-Means clustering is employed to distinguish salient edges attributable to the abdominal contour from weak edges due to the other texture. Subsequently, the iterative Hough transform is applied to determine an elliptical contour and obtain an initial estimation of the AC. Finally, the gradient vector field (GVF) snake adapts the initial ellipse to the real edges of the abdominal contour. Quantitative validation of the proposed method on synthetic images under different imaging conditions achieves satisfactory segmentation accuracy (98.78%+/-0.16%). Experiments on 150 clinical images are carried out in three aspects: comparisons between inter-observer and inter-run variation, the fitness analysis between the automatically detected ellipse and the manual delineation, and the accuracy comparisons between automatic measurements and manual measurements in estimation of fetal weight (EFW). Experimental results show that the proposed method can provide consistent and accurate measurements. The reductions of the mean absolute difference and the standard deviation of EFW based on automatic measurements are about 1.2% and 2.1%, respectively, which indicate its potential in clinical antepartum monitoring application.     

Segmentation in Echocardiographic Sequences using Shape-based Snake Model Combined with Generalized Hough Transformation

A novel method for segmentation of cardiac structures in temporal echocardiographic sequences based on the snake model is presented. The method is motivated by the observation that the structures of neighboring frames have consistent locations and shapes that aid in segmentation. To cooperate with the constraining information provided by the neighboring frames, we combine the template matching with the conventional snake model. It means that the model not only is driven by conventional internal and external forces, but also combines an additional constraint, the matching degree to measure the similarity between the neighboring prior shape and the derived contour. Furthermore, in order to auto or semi-automatically segment the sequent images without manually drawing the initial contours in each image, generalized Hough transformation (GHT) is used to roughly estimate the initial contour by transforming the neighboring prior shape. The method is particularly useful in case of the large frame-to-frame displacement of structure such as mitral valve. As a result, the active contour can easily detect the desirable boundaries in ultrasound images and has a high penetrability through the interference of various undesirables, such as the speckle, the tissue-related textures and the artifacts.     

Active ellipse model and automatic chamber detection in apical views of echocardiography images

In this article, an automatic method for detection of all chambers in apical two- and four-chamber views is proposed. The method is based on four evolving ellipses with their sizes and alignments (centre point) gradually changing through iterations until they reach to the point that approximates the chamber boundaries. The interaction between the internal, external and inter-elliptic forces controls the simultaneous evolution of ellipses. Since no prior assumption of the approximate location is required with our approach, the specialists are not required to locate the centre points of chambers in apical images, making the overall segmentation fully automated. Moreover, the resultant ellipse inside a chamber could be used as the initial contour in segmentation techniques such as active contour models, where the initial contour has a significant role for higher accuracy and faster convergence. The simplicity of equations developed in our approach make for a computationally faster algorithm, compared with former approaches that utilize morphologic operators. Our evolving ellipse does not go beyond the gaps, a problem that normally exists within boundaries in echo images, making our overall segmentation process more robust against the gaps. To evaluate the proposed method, a subset of 80 images is selected and three observers are requested to manually draw best ellipses inside the images and compare them with our results. The obtained dice coefficient results (87.62 ± 4.53% for observer-1, 83.18 ± 6.20% for observer-2, 86.02 ± 5.16% for observer-3) indicate that the proposed method has a useful performance.     

Discriminant snakes for 3D reconstruction of anatomical organs

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.     

一种使用膨胀气球模型的Snakes多目标分割方法

目的为实现一种有效的基于Snakes的多目标医学图像分割算法。方法通过模拟气球在含多个物体的封闭空间内的膨胀,来实现Snake曲线的拓扑形变。算法使用一个燃烧区域滞后跟踪Snake曲线运动来给出其演化停止条件,然后忽略停止条件并继续演化Snake曲线,就能完成燃烧而得到一个连通区域。提取出已燃烧区域的轮廓可给出与各分割目标对应的子Snake。结果所提出的算法能够正确分割出医学图像中多目标对象的轮廓。结论此基于模型的分割算法原理明确且易于实现,具有较好的实用性。     

A high-throughput active contour scheme for segmentation of histopathological imagery

In this paper a minimally interactive high-throughput system which employs a color gradient based active contour model for rapid and accurate segmentation of multiple target objects on very large images is presented. While geodesic active contours (GAC) have become very popular tools for image segmentation, they tend to be sensitive to model initialization. A second limitation of GAC models is that the edge detector function typically involves use of gray scale gradients; color images usually being converted to gray scale, prior to gradient computation. For color images, however, the gray scale gradient image results in broken edges and weak boundaries, since the other channels are not exploited in the gradient computation. To cope with these limitations, we present a new GAC model that is driven by an accurate and rapid object initialization scheme; hierarchical normalized cuts (HNCut). HNCut draws its strength from the integration of two powerful segmentation strategies—mean shift clustering and normalized cuts. HNCut involves first defining a color swatch (typically a few pixels) from the object of interest. A multi-scale, mean shift coupled normalized cuts algorithm then rapidly yields an initial accurate detection of all objects in the scene corresponding to the colors in the swatch. This detection result provides the initial contour for a GAC model. The edge-detector function of the GAC model employs a local structure tensor based color gradient, obtained by calculating the local min/max variations contributed from each color channel. We show that the color gradient based edge-detector function results in more prominent boundaries compared to the classical gray scale gradient based function. By integrating the HNCut initialization scheme with color gradient based GAC (CGAC), HNCut-CGAC embodies five unique and novel attributes: (1) efficiency in segmenting multiple target structures; (2) the ability to segment multiple objects from very large images; (3) minimal human interaction; (4) accuracy; and (5) reproducibility. A quantitative and qualitative comparison of the HNCut-CGAC model against other state of the art active contour schemes (including a Hybrid Active Contour model (Paragios–Deriche) and a region-based AC model (Rousson–Deriche)), across 196 digitized prostate histopathology images, suggests that HNCut-CGAC is able to outperform state of the art hybrid and region based AC techniques. Our results show that HNCut-CGAC is computationally efficient and may be easily applied to a variety of different problems and applications.     

Automatic Segmentation of Calcifications in Intravascular Ultrasound Images Using Snakes and the Contourlet Transform

It is valuable to detect calcifications in intravascular ultrasound images for studies of coronary artery diseases. An image segmentation method based on snakes and the Contourlet transform is proposed to automatically and accurately detect calcifications. With the Contourlet transform, an original image is decomposed into low-pass bands and band-pass directional sub-bands. The 2-D Renyi's entropy is used to adaptively threshold the low-pass bands in a multiresolution hierarchy to determine regions-of-interest (ROIs). Then a mean intensity ratio, reflecting acoustic shadowing, is presented to classify calcifications from noncalcifications and obtain initial contours of calcifications. The anisotropic diffusion is used in bandpass directional sub-bands to suppress noise and preserve calcific edges. Finally, the contour deformation in the boundary vector field is used to obtain final contours of calcifications. The method was evaluated via 60 simulated images and 86 in vivo images. It outperformed a recently proposed method, the Santos Filho method, by 2.76% and 14.53%, in terms of the sensitivity and specificity of calcification detection, respectively. The area under the receiver operating characteristic curve increased by 0.041. The relative mean distance error, relative difference degree, relative arc difference, relative thickness difference and relative length difference were reduced by 5.73%, 19.79%, 11.62%, 12.06% and 20.51%, respectively. These results reveal that the proposed method can automatically and accurately detect calcifications and delineate their boundaries. (E-mail: yywang@fudan.edu.cn)     

Segmentation of medical images using mean value guided contour

Partial differential equation-based (PDE-based) methods are extensively used in image segmentation, especially in contour models. Difficulties associated with the boundaries, namely troubles with developing initialization, inadequate convergence to boundary concavities, and difficulties connected to saddle points and stationary points of active contours make the contour models suffer from a feeble performance of referring to complex geometries. The present paper is designed to take advantage of mean value theorem rather than minimizing energy function for contours. It is efficiently capable of resolving above-mentioned problems by applying this theorem to the edge map gradient vectors, which is calculated from the image. Since the contour is computed in a straightforward manner from an edge map instead of force balance equation, it varies from other contour-based image segmentation methods. To illustrate the ability of the proposed model in complex geometries and ruptures, several experiments were also provided to validate the model. The experiments’ results demonstrated that the proposed method, which is called mean value guided contour (MVGC), is capable of repositioning contours into boundary concavities and has suitable forcefulness in complex geometries.     

Moment-based texture segmentation of luminal contour in intravascular ultrasound images

Purpose A system for luminal contour segmentation in intravascular ultrasound images is proposed. Methods Moment-based texture features are used for clustering of the pixels in the input image. After the clustering, morphological smoothing and a boundary detection process are applied and the final image is obtained. Results The proposed method was applied to 15 images from different patients, and a correlation coefficient of 0.86 was obtained between the areas of lumen automatically and manually defined. Conclusion Moment-based texture features together with the radial feature are powerful tools for identification of the lumen region in intravascular ultrasound images. Morphological filtering was useful for improving the segmentation results.     

Automatic initialization of an active shape model of the prostate

In this work is reported a new method for automatic segmentation of the boundary of the prostate, in transurethral ultrasound images. The scheme is based on a robust automatic initialization of an active shape model (ASM) of the prostate, which is subsequently fitted to the boundary of the gland. The initialization of the ASM is based on pixel classification to estimate the prostate region in an ultrasound image, followed by automatic adjustment – using a multipopulation genetic algorithm (MPGA) – of the initial pose of the ASM to the binary image produced by the classifier. The initial pose is next adjusted to the gray level ultrasound image, using the MPGA. After automatic initialization, the ASM is adjusted to the gray level ultrasound image to produce the final prostate contour. The method provides fast and robust segmentation of the prostate boundary. Validation results on 22 ultrasound images are reported with 1.74 mm of mean boundary error and an estimated processing time of 66 s per image. Our automatic initialization method can be applied with the ASMs of different organs in various imaging modalities.     

Application of a parallel spectral–spatial convolution neural network in object-oriented remote sensing land use classification

Due to the abundance of spatial information and relative lack of spectral information in high spatial resolution remote sensing images, a land use classification method for high-resolution remote sensing images is proposed based on a parallel spectral-spatial convolutional neural network (CNN) and object-oriented remote sensing technology. The contour of a remote sensing object is taken as the boundary and the set of pixels that comprises the object are extracted to form the input data set for the deep neural network. The proposed network considers both the features of the object and the pixels which forms the object. The spatial and spectral features of remote sensing image objects are extracted independently in the parallel network using panchromatic and multispectral remote sensing techniques. Then, through a fully connected layer, both spectral and spatial information are integrated to produce remote sensing object class coding. The experimental results demonstrate that the parallel spectral-spatial CNN, which combines spatial and spectral features, achieves better classification performance than the individual CNN. Therefore, the proposed method provides a novel approach to land use classification based on high spatial resolution remote sensing images.     

Semiautomatic contour detection in ultrasound M-mode images

We have developed a method for semiautomatic contour detection in M-mode images. The method combines tissue Doppler and grey-scale data. It was used to detect: 1. the left endocardium of the septum, the endocardium and epicardium of the posterior wall in 16 left ventricular short-axis M-modes, and 2. the mitral ring in 38 anatomical M-modes extracted pair-wise in 19 apical four-chamber cine-loops (healthy subjects). We validated the results by comparing the computer-generated contours with contours manually outlined by four echocardiographers. For all boundaries, the average distance between the computer-generated contours and the manual outlines was smaller than the average distance between the manual outlines. We also calculated left ventricular wall thickness and diameter at end-diastole and end-systole and lateral and septal mitral ring excursions, and found, on average, clinically negligible differences between the computer-generated indices and the same indices based on manual outlines (0.8-1.8 mm). The results were also within published normal values. In conclusion, this initial study showed that it was feasible in a robust and efficient manner to detect continuous wall boundaries in M-mode images so that tracings of left ventricular wall thickness, diameter and long axis could be derived.     

A Computerized System to Assess Axillary Lymph Node Malignancy from Sonographic Images

A computational approach to classifying axillary lymph node metastasis in sonographic images is described. One hundred five ultrasound images of axillary lymph nodes from patients with breast cancer were evaluated (81 benign and 24 malignant), and each lymph node was manually segmented, delineating both the whole lymph node and internal hilum surfaces. Normalized signed distance transforms were computed from the segmented boundaries of both structures, and each pixel was then assigned coordinates in a 3-D feature space according to the pixel's intensity, its signed distance to the node boundary and its signed distance to the hilum boundary. Three-dimensional histograms over the feature space were accumulated for each node by summing over all pixels, and the bin counts served as predictor inputs to a support vector machine learning algorithm. Repeated random sampling of 80/25 train/test splits was used to estimate generalization performance and generate receiver operating characteristic curves. The optimal classifier had an area under the receiver operating characteristic curve of 0.95 and sensitivity and specificity of 0.90 and 0.90. Our results indicate the feasibility of axillary nodal staging with computerized analysis.     

Probabilistic vessel axis tracing and its application to vessel segmentation with stream surfaces and minimum cost paths

We propose a novel framework to segment vessels on their cross-sections. It starts with a probabilistic vessel axis tracing in a gray-scale three-dimensional angiogram, followed by vessel boundary delineation on cross-sections derived from the extracted axis. It promotes a more intuitive delineation of vessel boundaries which are mostly round on the cross-sections. The prior probability density function of the axis tracer's formulation permits seamless integration of user guidance to produce continuous traces through regions that contain furcations, diseased portions, kissing vessels (vessels in close proximity to each other) and thin vessels. The contour that outlines the vessel boundary in a 3-D space is determined as the minimum cost path on a weighted directed acyclic graph derived from each cross-section. The user can place anchor points to force the contour to pass through. The contours obtained are tiled to approximate the vessel boundary surface. Since we use stream surfaces generated w.r.t. the traced axis as cross-sections, non-intersecting adjacent cross-sections are guaranteed. Therefore, the tiling can be achieved by joining vertices of adjacent contours. The vessel boundary surface is then deformed under constrained movements on the cross-sections and is voxelized to produce the final vascular segmentation. Experimental results on synthetic and clinical data have shown that the vessel axes extracted by our tracer are continuous and less jittered as compared with the other two trace-based algorithms. Furthermore, the segmentation algorithm with cross-sections are robust to noise and can delineate vessel boundaries that have level of variability similar to those obtained manually.