Rényi entropy of angular spread for detection of architectural distortion in prior mammograms

Abstract

This paper presents methods for the detection of architectural distortion in mammograms of interval-cancer cases taken prior to the diagnosis of breast cancer, using Rényi entropy. Initial candidates for sites of architectural distortion were detected using a bank of Gabor filters and phase portrait analysis. A total of 4,224 regions of interest (ROIs) were automatically obtained from 106 prior mammograms of 56 interval-cancer cases, including 301 true-positive ROIs, and from 52 mammograms of 13 normal cases. Each ROI was represented by the Rényi entropy of angular histograms composed with the Gabor magnitude response, angle, coherence, orientation strength, and the angular spread of power in the Fourier spectrum. Using the stepwise logistic regression and leave-one-image-out methods for feature selection, the best results achieved, in terms of the area under the receiver operating characteristic curve, are 0.72 with Fisher linear discriminant analysis and the Bayesian classifier, and 0.75 with an artificial neural network based on radial basis functions. Analysis of the free-response receiver operating characteristics indicated a sensitivity of 0.80 at 7.1 false positives per image.