Detecting clusters of microcalcifications with a cascade-based approach

Abstract

In this paper we present a cascade-based framework to detect clusters of microcalcifications on mammograms. The algorithm is based on a sliding window technique where a detector is structured as a "cascade" of simple boosting classifiers with increasing complexity. Such a method couples the effectiveness of the cascade approach with the RankBoost algorithm that is aimed at maximizing the area under the ROC curve and represents a good choice when dealing with unbalanced data sets. © 2012 Springer-Verlag Berlin Heidelberg.