Enhanced Multi-Level Thresholding Segmentation and Rank Based Region Selection for Detection of Masses in Mammograms

Abstract

A method for detection of masses in mammograms is presented. This method follows the general scheme of: (1) preprocessing of the image to increase the signal-to-noise ratio of the lesions being detected, (2) segmentation of all potential lesions, and (3) elimination of false-positive findings. An algorithm for enhancement of mammograms is proposed which has the objective of improving the segmentation of distinct structures in mammograms. The enhancement algorithm uses wavelet decomposition and reconstruction, morphological operations, and local scaling. After preprocessing, the segmentation of regions is performed via conversion to binary images at multiple threshold levels, and a set of features is computed from each of the segmented regions. A ranking system based on the features computed is also presented. This system is employed to select the regions representing abnormalities. The method was tested on 57 mammographic images of masses from the mini-MIAS database, including circumscribed, spiculated, and ill-defined masses. In this test, the proposed method achieved a sensitivity of 80% at 2.3 false-positives (FPs) per image.