Automatic classification of breast tissue

Authors:
Arnau Oliver;Jordi Freixenet;Anna Bosch;David Raba;Reyer Zwiggelaar
Affiliations:
Institute of Informatics and Applications, University of Girona, Spain;Institute of Informatics and Applications, University of Girona, Spain;Institute of Informatics and Applications, University of Girona, Spain;Institute of Informatics and Applications, University of Girona, Spain;Department of Computer Science, University of Wales, Wales, UK
Venue:
IbPRIA'05 Proceedings of the Second Iberian conference on Pattern Recognition and Image Analysis - Volume Part II
Year:
2005

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Abstract

A recent trend in digital mammography are CAD systems, which are computerized tools designed to help radiologists. Most of these systems are used for the automatic detection of abnormalities. However, recent studies have shown that their sensitivity is significantly decreased as the density of the breast is increased. In addition, the suitability of abnormality segmentation approaches tends to depend on breast tissue density. In this paper we propose a new approach to the classification of mammographic images according to the breast parenchymal density. Our classification is based on gross segmentation and the underlying texture contained within the breast tissue. Robustness and classification performance are evaluated on a set of digitized mammograms, applying different classifiers and leave-one-out for training. Results demonstrate the feasibility of estimating breast density using computer vision techniques.