Detection of masses in mammograms using texture features

Abstract

Suspicious regions are identified following the bilateral image subtraction of left and right breast image pairs. The study uses the nipple as a common rotational point thereby facilitating an alignment with the highest correlation prior to subtraction. Within this study, 144 breast images from the MIAS database are considered. Five co-occurrence matrices are constructed at four different distances for each suspicious region. Twelve texture features defined by Haralick et. al. (1973) are considered. Two further features defined by Chan et. al (1997), inertia and difference average, are also computed giving a total of fourteen texture measures. Following classification of six principal components calculated for the extracted features using an artificial neural network and 10-fold cross-validation, an average recognition rate of 77% was achieved. Using the receiver operating characteristic analysis, the overall sensitivity of the technique measured by the value of Az, was found to be 0.74.