DUGi

Automatic microcalcification and cluster detection for digital and digitised mammograms

http://dugi.udg.edu/item/http:@@@@hdl.handle.net@@2072@@296597

In this paper we present a knowledge-based approach for the automatic detection of microcalcifications and clusters in mammographic images. Our proposal is based on using local features extracted from a bank of filters to obtain a local description of the microcalcifications morphology. The developed approach performs an initial training step in order to automatically learn and select the most salient features, which are subsequently used in a boosted classifier to perform the detection of individual microcalcifications. Subsequently, the microcalcification detection method is extended in order to detect clusters. The validity of our approach is extensively demonstrated using two digitised databases and one full-field digital database. The experimental evaluation is performed in terms of ROC analysis for the microcalcification detection and FROC analysis for the cluster detection, resulting in better than 80% sensitivity at 1 false positive cluster per image

We would like to thank the reviewers for their critical evaluation of the manuscript. This study has been supported by the Ministerio de Ciencia e Innovacion under grants TIN2011-23704 and AYA2010-21782-C03-02. A.Torrent holds a FPU grant AP2007-01934. M. Tortajada holds a UdG grant BRGR10-04

Elsevier