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Automatic breast masses boundary extraction in digital mammography using spatial fuzzy c-means clustering and active contour models

Automatic breast masses boundary extraction in digital mammography using spatial fuzzy c-means clustering and active contour models,10.1109/MeMeA.2011

Automatic breast masses boundary extraction in digital mammography using spatial fuzzy c-means clustering and active contour models  
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In this paper, we propose a novel approach for the automatic breast boundary segmentation using spatial fuzzy c­ means clustering and active contours models. We will evaluate the performance of the approach on screen film mammographic images digitized by specific scanner devices and full-field digital mammographic images at different spatial and pixel resolutions. Expert radiologists have supplied the reference boundary for the massive lesions along with the biopsy proven pathology assess­ ment. A performance assessment procedure will be developed considering metrics such as precision, recall, F-measure, and accuracy of the segmentation results. A Montecarlo simulation will be also implemented to evaluate the sensitivity of the boundary extracted on the initial settings and on the image noise. ©2011 IEEE The presence of a mass is the most important mammo­ graphic sign of cancer. According to the Breast Imaging Reporting and Data Systems (BIRADS) (3), masses are space occupying lesions seen in at least two different projections. They are characterized by their shape (round, oval, lobular, irregular) and margins (circumscribed, microlobulated, ob­ scured, indistinct, spiculated). The mass boundary segmen­ tation is crucial for a reliable features extraction. Owing
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