Detection of microcalcifications in mammograms using 2D prediction filtering and a new statistical measure of the right tail weight

Abstract

In this paper, a new method to detect microcalcifications in mammograms is presented. The method is based on a candidate selection procedure which consists of a two-dimensional linear prediction adaptive filtering followed by a statistical parameter calculation developed by the authors and called tail ratio (TR). The parameter TR characterizes the presence of microcalcifications in a ROI, and is extracted from the local probability distribution within a small region surrounding each candidate. Afterward a group of new and previously published features are used to feed a neural network that classifies the candidates into microcalcification or non-microcalcification. The algorithm has been tested with 38 digitized mammograms obtaining a sensitivity of 0.93 for a positive predictive value of 0.88.