A Boosting Based Approach for Automatic Micro-calcification Detection

Abstract. 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.

• Type: Conference
• Year: 2010
• Where: MAMMO
• Authors: Arnau Oliver, Albert Torrent, Meritxell Tortajada, Xavier Lladó, Marta Peracaula, Lidia Tortajada, Melcior Sentís, Jordi Freixenet
• Keywords: Full-field Digital Database, Initial Training Step, MAMMO 2010, Medical Imaging, Well-known Digitised Mias