Please wait a minute...
FITEE
Front. Inform. Technol. Electron. Eng.  2010, Vol. 11 Issue (2): 111-118    DOI: 10.1631/jzus.C0910025
    
Automatic pectoral muscle boundary detection in mammograms based on Markov chain and active contour model
Lei WANG, Miao-liang ZHU, Li-ping DENG, Xin YUAN*
School of Computer Science and Technology, Zhejiang University, Hangzhou 310027, China
Automatic pectoral muscle boundary detection in mammograms based on Markov chain and active contour model
Lei WANG, Miao-liang ZHU, Li-ping DENG, Xin YUAN*
School of Computer Science and Technology, Zhejiang University, Hangzhou 310027, China
 全文: PDF(609 KB)  
摘要： Automatic pectoral muscle removal on medio-lateral oblique (MLO) view of mammogram is an essential step for many mammographic processing algorithms. However, it is still a very difficult task since the sizes, the shapes and the intensity contrasts of pectoral muscles change greatly from one MLO view to another. In this paper, we propose a novel method based on a discrete time Markov chain (DTMC) and an active contour model to automatically detect the pectoral muscle boundary. DTMC is used to model two important characteristics of the pectoral muscle edge, i.e., continuity and uncertainty. After obtaining a rough boundary, an active contour model is applied to refine the detection results. The experimental results on images from the Digital Database for Screening Mammography (DDSM) showed that our method can overcome many limitations of existing algorithms. The false positive (FP) and false negative (FN) pixel percentages are less than 5% in 77.5% mammograms. The detection precision of 91% meets the clinical requirement.
关键词： Pectoral muscle;  Markov chain;  Active contour;  Mammogram    
Abstract: Automatic pectoral muscle removal on medio-lateral oblique (MLO) view of mammogram is an essential step for many mammographic processing algorithms. However, it is still a very difficult task since the sizes, the shapes and the intensity contrasts of pectoral muscles change greatly from one MLO view to another. In this paper, we propose a novel method based on a discrete time Markov chain (DTMC) and an active contour model to automatically detect the pectoral muscle boundary. DTMC is used to model two important characteristics of the pectoral muscle edge, i.e., continuity and uncertainty. After obtaining a rough boundary, an active contour model is applied to refine the detection results. The experimental results on images from the Digital Database for Screening Mammography (DDSM) showed that our method can overcome many limitations of existing algorithms. The false positive (FP) and false negative (FN) pixel percentages are less than 5% in 77.5% mammograms. The detection precision of 91% meets the clinical requirement.
Key words: Pectoral muscle    Markov chain    Active contour    Mammogram
收稿日期: 2009-01-09 出版日期: 2010-01-01
CLC:  TP391.4  
基金资助: Project (No. 60505009) supported by the National Natural Science Foundation of China
通讯作者: Xin YUAN     E-mail: yxxinyuan@zju.edu.cn
服务　　
把本文推荐给朋友
加入引用管理器
E-mail Alert
RSS
作者相关文章　　
Lei WANG
Miao-liang ZHU
Li-ping DENG
Xin YUAN

引用本文:

Lei WANG, Miao-liang ZHU, Li-ping DENG, Xin YUAN. Automatic pectoral muscle boundary detection in mammograms based on Markov chain and active contour model. Front. Inform. Technol. Electron. Eng., 2010, 11(2): 111-118.

链接本文:

http://www.zjujournals.com/xueshu/fitee/CN/10.1631/jzus.C0910025    或    http://www.zjujournals.com/xueshu/fitee/CN/Y2010/V11/I2/111

[1] Xin Hao, Ye Shen, Shun-ren Xia. Automatic mass segmentation on mammograms combining random walks and active contour[J]. Front. Inform. Technol. Electron. Eng., 2012, 13(9): 635-648.
[2] Xiao-lei Ma, Yin-hai Wang, Feng Chen, Jian-feng Liu. Transit smart card data mining for passenger origin information extraction[J]. Front. Inform. Technol. Electron. Eng., 2012, 13(10): 750-760.
[3] Gloria Bueno, Oscar Déniz, Jesús Salido, Carmen Carrascosa, José M. Delgado. Three-dimensional organ modeling based on deformable surfaces applied to radio-oncology[J]. Front. Inform. Technol. Electron. Eng., 2010, 11(6): 407-417.