Pectoral muscle segmentation in mammograms based on homogenous texture and intensity deviation

Authors:
Yanfeng Li;Houjin Chen;Yongyi Yang;Na Yang
Affiliations:
School of Electronic and Information Engineering, Beijing Jiaotong University, Beijing, China;School of Electronic and Information Engineering, Beijing Jiaotong University, Beijing, China;Department of Electrical and Computer Engineering, Illinois Institute of Technology, Chicago, USA;School of Electronic and Information Engineering, Beijing Jiaotong University, Beijing, China
Venue:
Pattern Recognition
Year:
2013

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Abstract

In this paper, a novel method is proposed to segment the pectoral muscle in mammograms. First two anatomical features of the pectoral muscle, homogeneous texture and high intensity deviation are employed to identify the initial pectoral muscle edge. Then Kalman filter is used to refine the ragged initial edge. The proposed method is tested on Mammographic Image Analysis Society Mini-Mammographic (mini-MIAS) database and Digital Database for Screening Mammography (DDSM) database. The acceptable rate is 90.06% and 92% for the mini-MIAS database and the DDSM database, respectively.