Abstract
Automatic detection and identification of mammography masses is important for breast cancer diagnosis. However, it is challenging to differentiate masses from normal breast regions because they usually have low contrast and a poor boundary. In this study, we present a Computer-Aided Detection (CAD) system for automatic breast mass identification. A four-stage region-based procedure is adopted for processing the mammogram images, i.e. localization, segmentation, feature extraction, and feature selection and classification. The proposed CAD system is evaluated using selected mammogram images from the Mammographic Image Analysis Society (MIAS) database. The experimental results demonstrate that the proposed CAD system is able to identify mammography masses in an automated manner, and is useful as a decision support system for breast cancer diagnosis.
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Samma, H., Lim, C.P., Samma, A. (2010). A Computer-Aided Detection System for Automatic Mammography Mass Identification. In: Wong, K.W., Mendis, B.S.U., Bouzerdoum, A. (eds) Neural Information Processing. Models and Applications. ICONIP 2010. Lecture Notes in Computer Science, vol 6444. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-17534-3_28
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DOI: https://doi.org/10.1007/978-3-642-17534-3_28
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