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Front. Inform. Technol. Electron. Eng.  2012, Vol. 13 Issue (9): 635-648    DOI: 10.1631/jzus.C1200052
    
Automatic mass segmentation on mammograms combining random walks and active contour
Xin Hao, Ye Shen, Shun-ren Xia
MOE Key Laboratory of Biomedical Engineering, Zhejiang University, Hangzhou 310027, China; Department of Signal and Information Processing, China Jiliang University, Hangzhou 310018, China
Automatic mass segmentation on mammograms combining random walks and active contour
Xin Hao, Ye Shen, Shun-ren Xia
MOE Key Laboratory of Biomedical Engineering, Zhejiang University, Hangzhou 310027, China; Department of Signal and Information Processing, China Jiliang University, Hangzhou 310018, China
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摘要： 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    
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.
Key words: Active contour    Random walks    Mass segmentation    Mammogram
收稿日期: 2012-03-06 出版日期: 2012-09-05
CLC:  TP391.41  
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引用本文:

Xin Hao, Ye Shen, Shun-ren Xia. Automatic mass segmentation on mammograms combining random walks and active contour. Front. Inform. Technol. Electron. Eng., 2012, 13(9): 635-648.

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http://www.zjujournals.com/xueshu/fitee/CN/10.1631/jzus.C1200052    或    http://www.zjujournals.com/xueshu/fitee/CN/Y2012/V13/I9/635

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