Combining registration and abnormality detection in mammography

Authors:
Mohamed Hachama;Agnès Desolneux;Frédéric Richard
Affiliations:
MAP5, University Paris 5, Paris, France;MAP5, University Paris 5, Paris, France;MAP5, University Paris 5, Paris, France
Venue:
WBIR'06 Proceedings of the Third international conference on Biomedical Image Registration
Year:
2006

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Abstract

Usually, image registration and abnormality detection (e.g. lesions) in mammography are solved separately, although the solutions of these problems are strongly dependent. In this paper, we introduce a Bayesian approach to simultaneously register images and detect abnormalities. The key idea is to assume that pixels can be divided into two classes: normal tissue and abnormalities. We define the registration constraints as a mixture of two distributions which describe statistically image gray-level variations for both pixel classes. These mixture distributions are weighted by a map giving probabilities of abnormalities to be present at each pixel position. Using the Maximum A Posteriori, we estimate the deformation and the abnormality map at the same time. We show some experiments which illustrate the performance of this method in comparison to some previous techniques.