Towards automated segmentation and classification of masses in mammograms.

This paper presents a straightforward approach to detecting and segmenting mammographic mass cores. The method utilizes adaptive thresholding applied to a contrast-enhanced version of the gray-scale mammogram, where the threshold is a function of the localized gray-level mean and variance. To assess the method's efficacy, it is applied to a database of 62 mammograms, each containing a suspicious mass (39 benign and 23 malignant). Each test case consists of a gray-scale image and a binary image containing a radiologist segmentation of the mass. After segmentation, a variety of features are extracted, including several based on the normalized radial length, rubber band straightening algorithm, gray-level statistics, and patient age. Next, step-wise linear discriminant analysis is utilized for feature reduction and optimization. The same procedure is applied to the manually segmented masses. Analysis of the optimized features resulted in an ROC curve area of Az = 0.8796 and Az = 0.8719 for the automated and manually segmented masses, respectively.