Automatic microcalcification and cluster detection for digital and digitised mammograms

Authors:
Arnau Oliver;Albert Torrent;Xavier Lladó;Meritxell Tortajada;Lidia Tortajada;Melcior Sentís;Jordi Freixenet;Reyer Zwiggelaar
Affiliations:
Department of Computer Architecture and Technology, University of Girona, 17071 Girona, Spain;Department of Computer Architecture and Technology, University of Girona, 17071 Girona, Spain;Department of Computer Architecture and Technology, University of Girona, 17071 Girona, Spain;Department of Computer Architecture and Technology, University of Girona, 17071 Girona, Spain;UDIAT-Centre Diagnòstic, Corporació Parc Taulí, 08208 Sabadell, Spain;UDIAT-Centre Diagnòstic, Corporació Parc Taulí, 08208 Sabadell, Spain;Department of Computer Architecture and Technology, University of Girona, 17071 Girona, Spain;Department of Computer Science, University of Wales, Aberystwyth SY23 3DB, UK
Venue:
Knowledge-Based Systems
Year:
2012

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Abstract

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.