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• W2131213765 abstract "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." @default.
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• W2131213765 date "2014-05-01" @default.
• W2131213765 modified "2023-09-25" @default.
• W2131213765 title "A novel automatic suspicious mass regions identification using Havrda & Charvat entropy and Otsu's N thresholding" @default.
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• W2131213765 doi "https://doi.org/10.1016/j.cmpb.2014.02.014" @default.
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