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Automatic pectoral muscle boundary detection in mammograms based on Markov chain and active contour model

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Journal of Zhejiang University-Science C(Computers and Electronics) 2010年第2期11卷 111-118页

作者： Lei WANG Miao-liang ZHU Li-ping DENG Xin YUAN School of Computer Science and Technology Zhejiang University Hangzhou 310027 China

Automatic pectoral muscle removal on medio-lateral oblique (MLO) view of mammogram is an essential step for many mammographic processing algorithms. However,it is still a very difficult task since the sizes,the shapes and the intensity contrasts of pectoral muscles change greatly from one MLO view to another. In this paper,we propose a novel method based on a discrete time Markov chain (DTMC) and an active contour model to automatically detect the pectoral muscle boundary. DTMC is used to model two important characteristics of the pectoral muscle edge,i.e.,continuity and uncertainty. After obtaining a rough boundary,an active contour model is applied to refine the detection results. The experimental results on images from the Digital Database for Screening Mammography (DDSM) showed that our method can overcome many limitations of existing algorithms. The false positive (FP) and false negative (FN) pixel percentages are less than 5% in 77.5% mammograms. The detection precision of 91% meets the clinical requirement.

关键词： Pectoral muscle Markov chain active contour Mammogram

active contour driven by local entropy energy function for segmentation and bias correction

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Active contour driven by local entropy energy function for s...

Automatic Anterior Lamina Cribrosa Surface Depth Measurement Based on active contour and Energy Constraint

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作者： Gai Pan Liqun Gao Zhaohua Cui College of Information Science and Engineering Northeastern University Unit 65041

ISBN: (纸本)9781467355339

C-V model has poor segmentation of images with intensity inhomogeneity. To overcome that problem, a novel active contour driven by a local entropy energy function is proposed to segment images with intensity inhomogeneity and get bias corrected images. The main idea of this paper is entropy can measure the degree of intensity homogeneity and local intensity information is extracted due to a weight function. Simulation experiments of 3 images show: this method can deal with intensity inhomogeneity problem of C-V model, and has better adaptability to initial location of the contour curve.

关键词： C-V Model active contour Entropy Intensity Inhomogeneity

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Journal of Computer Science & Technology 2017年第6期32卷 1214-1221页

作者： Zai-Liang Chen Peng Peng Bei-Ji Zou Hai-Lan Shen Hao Wei Rong-Chang Zhao School of Information Science and Engineering Central South University Changsha 410083 China Center for Ophthalmic Imaging Research Central South University Changsha 410083 China 'Mobile Health' Ministry of Education-China Mobile Joint Laboratory Central South University Changsha 410083 China

The lamina cribrosa is affected by intraocular pressure, which is the major risk of glaucoma. However, the capability to evaluate the lamina cribrosa in vivo has been limited until recently due to poor image quality and the posterior laminar displacement of glaucomatous eyes. In this study, we propose an automatic method to measure the anterior lamina cribrosa surface depth （ALCSD）, including a method for detecting Bruch＇s membrane opening （BMO） based on k-means and region-based active contour. An anterior lamina cribrosa surface segmentation method based on energy constraint is also proposed. In BMO detection, we initialize the Chan-Vese active contour model by using the segmentation map of the k-means cluster. In the segmentation of anterior lamina cribrosa surface, we utilize the energy function in each A-scan to establish a set of candidates. The points in the set that fail to meet the constraints are removed. Finally, we use the B-spline fitting method to obtain the results. The proposed automatic method can model the posterior laminar displacement by measuring the ALCSD. This method achieves a mean error of 45.34 μm in BMO detection. The mean errors of the anterior lamina cribrosa surface are 94.1% within five pixels and 76.1% within three pixels.

关键词： anterior lamina cribrosa surface segmentation active contour energy constraint measurement

Using shapes correlation for active contour segmentation of uterine fibroid ultrasound images in computer-aided therapy

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Applied Mathematics(A Journal of Chinese Universities) 2016年第1期31卷 37-52页

作者： NI Bo HE Fa-zhi PAN Yi-teng YUAN Zhi-yong School of Computer Science Wuhan University School of Computer Science Hubei Polytechnic University

Segmenting the lesion regions from the ultrasound （US） images is an important step in the intra-operative planning of some computer-aided therapies. High-Intensity Focused Ultrasound （HIFU）, as a popular computer-aided therapy, has been widely used in the treatment of uterine fibroids. However, such segmentation in HIFU remains challenge for two reasons：（1） the blurry or missing boundaries of lesion regions in the HIFU images and （2） the deformation of uterine fibroids caused by the patient＇s breathing or an external force during the US imaging process, which can lead to complex shapes of lesion regions. These factors have prevented classical active contour-based segmentation methods from yielding desired results for uterine fibroids in US images. In this paper, a novel active contour-based segmentation method is proposed, which utilizes the correlation information of target shapes among a sequence of images as prior knowledge to aid the existing active contour method. This prior knowledge can be interpreted as a unsupervised clustering of shapes prior modeling. Meanwhile, it is also proved that the shapes correlation has the low-rank property in a linear space, and the theory of matrix recovery is used as an effective tool to impose the proposed prior on an existing active contour model. Finally, an accurate method is developed to solve the proposed model by using the Augmented Lagrange Multiplier （ALM）. Experimental results from both synthetic and clinical uterine fibroids US image sequences demonstrate that the proposed method can consistently improve the performance of active contour models and increase the robustness against missing or misleading boundaries, and can greatly improve the efficiency of HIFU therapy.

关键词： active contour shapes correlation ultrasound image segmentation matrix recovery computer-aided therapy.

Automated Segmentation of Left Ventricle Using Local and Global Intensity Based active contour and Dynamic Programming

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International Journal of Automation and computing 2018年第6期15卷 673-688页

作者： G.Dharanibai Anupama Chandrasekharan Zachariah C.Alex School of Electronics Engineering Vellore Institute of Technology (VIT) University Department of Radiology Sri Ramachandra University

The aim of this work is to develop an improved region based active contour and dynamic programming based method for accurate segmentation of left ventricle （LV） from multi-slice cine short axis cardiac magnetic resonance （MR） images. Intensity inhomogeneity and weak object boundaries present in MR images hinder the segmentation accuracy. The proposed active contour model driven by a local Gaussian distribution fitting （LGDF） energy and an auxiliary global intensity fitting energy improves the accuracy of endocardial boundary detection. The weightage of the global energy fitting term is dynamically adjusted using a spatially varying weight function. Dynamic programming scheme proposed for the segmentation of epicardium considers the myocardium probability map and a distance weighted edge map in the cost matrix. Radial distance weighted technique and conical geometry are employed for segmenting the basal slices with left ventricle outflow tract （LVOT） and most apical slices. The proposed method is validated on a public dataset comprising 45 subjects from medical image computing and computer assisted interventions （MICCAI） 2009 segmentation challenge. The average percentage of good endocardial and epicardial contours detected is about 99%, average perpendicular distance of the detected good contours from the manual reference contours is 1.95 mm, and the dice similarity coefficient between the detected contours and the reference contours is 0.91. Correlation coefficient and the coefficient of determination between the ejection fraction measurements from manual segmentation and the automated method are respectively 0.9781 and 0.9567, for LV mass these values are 0.9249 and 0.8554. Statistical analysis of the results reveals a good agreement between the clinical parameters determined manually and those estimated using the automated method.

关键词： Cardiovascular magnetic resonance left ventricle endocardium epicardium myocardium segmentation active contour dynamic programming.

Automatic mass segmentation on mammograms combining random walks and active contour

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Journal of Zhejiang University-Science C(Computers and Electronics) 2012年第9期13卷 635-648页

作者： Xin HAO Ye SHEN Shun-ren XIA MOE Key Laboratory of Biomedical Engineering Zhejiang UniversityHangzhou 310027China Department of Signal and lnformation Processing China Jiliang UniversityHangzhou 310018China

Accurate mass segmentation on mammograms is a critical step in computer-aided diagnosis (CAD) systems. It is also a challenging task since some of the mass lesions are embedded in normal tissues and possess poor contrast or ambiguous margins. Besides, the shapes and densities of masses in mammograms are various. In this paper, a hybrid method combining a random walks algorithm and Chan-Vese (CV) active contour is proposed for automatic mass segmentation on mammograms. The data set used in this study consists of 1095 mass regions of interest (ROIs). First, the original ROI is preprocessed to suppress noise and surrounding tissues. Based on the preprocessed ROI, a set of seed points is generated for initial random walks segmentation. Afterward, an initial contour of mass and two probability matrices are produced by the initial random walks segmentation. These two probability matrices are used to modify the energy function of the CV model for prevention of contour leaking. Lastly, the final segmentation result is derived by the modified CV model, during which the probability matrices are updated by inserting several rounds of random walks. The proposed method is tested and compared with other four methods. The segmentation results are evaluated based on four evaluation metrics. Experimental results indicate that the proposed method produces more accurate mass segmentation results than the other four methods.

关键词： active contour Random walks Mass segmentation Mammogram

A Novel active contour Model for Medical Image Segmentation

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Journal of Shanghai Jiaotong university(Science) 2010年第5期15卷 549-555页

作者：付增良叶铭苏永琳林艳萍王成焘 Institute of Biomedical Manufacturing and Life Quality Engineering Shanghai Jiaotong University

A novel segmentation method for medical image with intensity inhomogeneity is *** the proposed active contour model,both region and gradient information are taken into *** former,i.e.,region-based fitting energy,draws upon the region information and guarantees the accurate extraction of inhomogeneous image's local *** latter,i.e.,an edge indicator,weights the length penalizing term to consider the gradient ***,signed distance penalizing term is also added to ensure accurate computation and avoid the time-consuming re-initialization of evolving level set *** for synthetic and real images demonstrate the feasibility and superiority of the proposed model.

关键词： image segmentation active contour intensity inhomogeneity region-based fitting energy edge indicator

Fast Texture Segmentation Based on Semi-Local Region Descriptor and active contour

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Numerical Mathematics(Theory,Methods and Applications) 2009年第4期2卷 445-468页

作者： Nawal Houhou Jean-Philippe Thiran Xavier Bresson Signal Processing Laboratory 5 Ecole Polytechnique Federale de Lausanne （EPFL） Lausanne Switzerland. Department of Mathematics University of California Los Angeles CA 90095-1555 USA.

In this paper, we present an efficient approach for unsupervised segmentation of natural and textural images based on the extraction of image features and a fast active contour segmentation model. We address the problem of textures where neither the gray-level information nor the boundary information is adequate for object extraction. This is often the case of natural images composed of both homogeneous and textured regions. Because these images cannot be in general directly processed by the gray-level information, we propose a new texture descriptor which intrinsically defines the geometry of textures using semi-local image information and tools from differential geometry. Then, we use the popular Kullback-Leibler distance to design an active contour model which distinguishes the background and textures of interest. The existence of a minimizing solution to the proposed segmentation model is proven. Finally, a texture segmentation algorithm based on the Split-Bregrnan method is introduced to extract meaningful objects in a fast way. Promising synthetic and real-world results for gray-scale and color images are presented.

关键词： Semi-local image information Beltrami framework metric tensor active contour Kullback-Leibler distance split-Bregman method.

Novel active contour Model for Image Segmentation Based on Local Fuzzy Gaussian Distribution Fitting

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Journal of Electronic Science and Technology 2012年第2期10卷 113-118页

作者： Quang Tung Thieu Marie Luong Jean-Marie Rocchisani Nguyen Linh-Trung Emmanuel Viennet the L2TI laboratory Université Paris 13 Sorbonne Paris CitéF-93430 Villetaneuse France the Avicenne University Hospital the Faculty of Electronics and Telecommunications University of Engineering and Technology Vietnam National University

A novel active contour model is proposed, which incorporates local information distributions in a fuzzy energy function to effectively deal with the intensity inhomogeneity. Moreover, the proposed model is convex with respect to the variable which is used for extracting the contour. This makes the model independent on the initial condition and suitable for an automatic segmentation. Furthermore, the energy function is minimized in a computationally efficient way by calculating the fuzzy energy alterations directly. Experiments are carried out to prove the performance of the proposed model over some existing methods. The obtained results confirm the efficiency of the method.

关键词： active contour energy minimization fuzzy energy function local information medical image segmentation.