Abstract
Breast cancer causes numerous deaths worldwide; yet the numbers have decreased in the past years as a result of computer-aided diagnosis and proper treatment. The current paper is addressed to the base of such diagnosis system: pre-processing and segmentation. After a robust pre-processing, an unsupervised version of GrowCut is applied to define the location of the abnormality. We present a method to automatically define the foreground seeds used in GrowCut. For experiments, mammograms from mini-MIAS dataset are used and a precision of 93.63% for the foreground seeds masks is achieved, which leads to promising segmentation results.
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