Detection of masses in mammograms via statistically based enhancement, multilevel-thresholding segmentation, and region selection

Abstract

A method for automatic detection of mammographic masses is presented. As part of this method, an enhancement algorithm that improves image contrast based on local statistical measures of the mammograms is proposed. After enhancement, regions are segmented via thresholding at multiple levels, and a set of features is computed from each of the segmented regions. A region-ranking system is also presented that identifies the regions most likely to represent abnormalities based on the features computed. The method was tested on 57 mammographic images of masses from the Mini-MIAS database, and achieved a sensitivity of 80% at 2.3 false-positives per image (average of 0.32 false-positives per image).