Automatic mass segmentation on mammograms combining random walks and active contour

Xin HAO; Ye SHEN; Shun-ren XIA

doi:10.1631/jzus.C1200052

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Automatic mass segmentation on mammograms combining random walks and active contour

Computer & Automation | Updated：2022-12-09

- Automatic mass segmentation on mammograms combining random walks and active contour
- Automatic mass segmentation on mammograms combining random walks and active contour
- Frontiers of Information Technology & Electronic Engineering Vol. 13, Issue 9, Pages: 635-648(2012)
- Affiliations：
  
  1.MOE Key Laboratory of Biomedical Engineering, Zhejiang University, Hangzhou 310027, China
  2.Department of Signal and Information Processing, China Jiliang University, Hangzhou 310018, China
- Author bio：
  
  [ "Xin HAO, E-mail: sarah.xin@gmail.com" ]
  Shun-ren XIA, E-mail: srxia@zju.edu.cn
- Funds：
- DOI：10.1631/jzus.C1200052
  CLC： TP391.41
- Received：06 March 2012，
  
  Revised：2012-;8-;3，
  
  Published：2012-09
- Accepted：
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Xin HAO, Ye SHEN, Shun-ren XIA. Automatic mass segmentation on mammograms combining random walks and active contour[J]. Frontiers of Information Technology & Electronic Engineering, 2012, 13(9): 635-648.
DOI：

Xin HAO, Ye SHEN, Shun-ren XIA. Automatic mass segmentation on mammograms combining random walks and active contour[J]. Frontiers of Information Technology & Electronic Engineering, 2012, 13(9): 635-648. DOI： 10.1631/jzus.C1200052.

摘要

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.

关键词

Keywords

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