A Boosting Based Approach for Automatic Micro-calcification Detection

Abstract

AbstractIn 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.KeywordsDigital MammographyMammographic ImageDigital MammogramDigital DatabaseDictionary WordThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.