Automatic mass segmentation on mammograms combining random walks and active contour

• Published in: Frontiers of information technology & electronic engineering Vol. 13; no. 9; pp. 635 - 648
• Main Authors: Hao, Xin, Shen, Ye, Xia, Shun-ren
• Format: Journal Article
• Language: English
• Published: Heidelberg SP Zhejiang University Press 01.09.2012; Springer Nature B.V
• Subjects: Algorithms; Communications Engineering; Computer Hardware; Computer Science; Computer Systems Organization and Communication Networks; Contours; 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; Mammogram; TP391.41; Random walks; Mass segmentation; Active contour
• ISSN: 1869-1951; 2095-9184; 1869-196X; 2095-9230
• DOI: 10.1631/jzus.C1200052

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.