Automatic mass segmentation on mammograms combining random walks and active contour

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...

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Bibliographic Details
Published in:Journal of Zhejiang University. C Science 2012-09, Vol.13 (9), p.635-648
Main Authors: Hao, Xin, Shen, Ye, Xia, Shun-ren
Format: Article
Language:English
Subjects:
Communications Engineering
Computer Hardware
Computer Science
Computer Systems Organization and Communication Networks
Density
Electrical Engineering
Electronics and Microelectronics
Instrumentation
Mathematical analysis
Matrices
Matrix methods
Networks
Random walk
Segmentation
Shape
Tasks
X光检查
主动轮廓线
乳房
分割
动质量
投资回报率
计算机辅助诊断
随机游动
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Summary: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.
ISSN:1869-1951
1869-196X
DOI:10.1631/jzus.C1200052