Comput Methods Programs Biomed - A novel automatic suspicious mass regions identification using Havrda & Charvat entropy and Otsu's N thresholding.

Tópicos

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Resumo

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.

Resumo Limpo

mass detect import process breast cancer diagnosi comput aid system can complex mass small invis dens breast tissu therefor extract suspici mass region can challeng paper propos novel segment algorithm identifi mass candid region mammogram propos system includ three part breast region pector muscl segment imag enhanc suspici mass region identif first two part examin previous studi studi focus suspici mass region identif use combin havrda charvat entropi method otsus n threshold method open access mammograph imag analysi societi mia databas contain mass use studi propos system obtain sensit rate suspici mass region identif abnorm normal imag

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