A novel automatic suspicious mass regions identification using Havrda & Charvat entropy and Otsu's N thresholding

Kurt B., Nabiyev V. V., Turhan K.

COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE, vol.114, no.3, pp.349-360, 2014 (SCI-Expanded) identifier identifier identifier

  • Publication Type: Article / Article
  • Volume: 114 Issue: 3
  • Publication Date: 2014
  • Doi Number: 10.1016/j.cmpb.2014.02.014
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Page Numbers: pp.349-360
  • Keywords: Breast region segmentation, Pectoral muscle segmentation, Image enhancement, Suspicious mass regions identification, Havrda & Charvat entropy, Otsu's N thresholding, SEGMENTATION
  • Karadeniz Technical University Affiliated: Yes


Mass detection is a very important process for breast cancer diagnosis and computer aided systems. It can be very complex when the mass is small or invisible because of dense breast tissue. Therefore, the extraction of suspicious mass region can be very challenging. This paper proposes a novel segmentation algorithm to identify mass candidate regions in mammograms. The proposed system includes three parts: breast region and pectoral muscle segmentation, image enhancement and suspicious mass regions identification. The first two parts have been examined in previous studies. In this study, we focused on suspicious mass regions identification using a combination of Havrda & Charvat entropy method and Otsu's N thresholding method. An open access Mammographic Image Analysis Society (MIAS) database, which contains 59 masses, was used for the study. The proposed system obtained a 93% sensitivity rate for suspicious mass regions identification in 56 abnormal and 40 normal images. (C) 2014 Elsevier Ireland Ltd. All rights reserved.