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A novel method for accurate detection of regions of interest (ROIs) that contain circumscribed lesions in mammograms is presented. The mammograms are segmented using a statistical threshold and a number of candidate regions are extracted. Then a set of qualification criteria is employed to filter these regions retaining the most suspicious for which a Radial-Basis Function Neural Network makes the final decision marking them as ROIs that contain abnormal tissue. The proposed method detects the exact location of the circumscribed lesions with accuracy of 90.9%, and a very low number of false positive regions per image (2.1 ROIs per image) in the MIAS database.

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