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CLC number: TP391.41

On-line Access: 2012-09-05

Received: 2012-03-06

Revision Accepted: 2012-07-12

Crosschecked: 2012-08-03

Cited: 7

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Journal of Zhejiang University SCIENCE C 2012 Vol.13 No.9 P.635-648

10.1631/jzus.C1200052


Automatic mass segmentation on mammograms combining random walks and active contour


Author(s):  Xin Hao, Ye Shen, Shun-ren Xia

Affiliation(s):  MOE Key Laboratory of Biomedical Engineering, Zhejiang University, Hangzhou 310027, China; more

Corresponding email(s):   sarah.xin@gmail.com, srxia@zju.edu.cn

Key Words:  Active contour, Random walks, Mass segmentation, Mammogram


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Xin Hao, Ye Shen, Shun-ren Xia. Automatic mass segmentation on mammograms combining random walks and active contour[J]. Journal of Zhejiang University Science C, 2012, 13(9): 635-648.

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%I Zhejiang University Press & Springer
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T1 - Automatic mass segmentation on mammograms combining random walks and active contour
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Abstract: 
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.

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