Journal of Digital Imaging

, Volume 31, Issue 5, pp 680–691 | Cite as

Automatic Pectoral Muscle Region Segmentation in Mammograms Using Genetic Algorithm and Morphological Selection

  • Rongbo Shen
  • Kezhou Yan
  • Fen Xiao
  • Jia Chang
  • Cheng Jiang
  • Ke Zhou


In computer-aided diagnosis systems for breast mammography, the pectoral muscle region can easily cause a high false positive rate and misdiagnosis due to its similar texture and low contrast with breast parenchyma. Pectoral muscle region segmentation is a crucial pre-processing step to identify lesions, and accurate segmentation in poor-contrast mammograms is still a challenging task. In order to tackle this problem, a novel method is proposed to automatically segment pectoral muscle region in this paper. The proposed method combines genetic algorithm and morphological selection algorithm, incorporating four steps: pre-processing, genetic algorithm, morphological selection, and polynomial curve fitting. For the evaluation results on different databases, the proposed method achieves average FP rate and FN rate of 2.03 and 6.90% (mini MIAS), 1.60 and 4.03% (DDSM), and 2.42 and 13.61% (INBreast), respectively. The results can be comparable performance in various metrics over the state-of-the-art methods.


Breast mammography Pectoral muscle region segmentation Genetic algorithm Morphological selection 


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Copyright information

© Society for Imaging Informatics in Medicine 2018

Authors and Affiliations

  1. 1.Key Laboratory of Information Storage System, Wuhan National Laboratory for OptoelectronicsHuazhong University of Science and TechnologyWuhanPeople’s Republic of China
  2. 2.Tencent Inc.ShenzhenChina

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