A Boosting Based Approach for Automatic Micro-calcification Detection

In this paper we present a boosting based approach for automatic detection of micro-calcifications in mammographic images. Our proposal is based on using local features extracted from a bank of filters for obtaining a description of the different micro-calcifications morphology. The approach performs an initial training step in order to automatically learn and select the most salient features, which are subsequently used in a boosting classifier to perform the detection. The validity of our method is demonstrated using 112 mammograms of the well-known digitised MIAS database and 280 mammograms of a full-field digital database. The experimental evaluation is performed in terms of ROC analysis, obtaining Az = 0.88 and Az = 0.90 respectively, and FROC analysis. The obtained results show the feasibility of our approach for detecting micro-calcifications in both digitised and digital technologies.