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.
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Hachama, M., Desolneux, A., Richard, F. (2006). Combining Registration and Abnormality Detection in Mammography. In: Pluim, J.P.W., Likar, B., Gerritsen, F.A. (eds) Biomedical Image Registration. WBIR 2006. Lecture Notes in Computer Science, vol 4057. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11784012_22
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DOI: https://doi.org/10.1007/11784012_22
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