Detection, Synthesis and Compression in Mammographic Image Analysis with a Hierarchical Image Probability Model

Authors:
Clay Spence;Lucas Parra;Paul Sajda
Affiliations:
-;-;-
Venue:
MMBIA '01 Proceedings of the IEEE Workshop on Mathematical Methods in Biomedical Image Analysis (MMBIA'01)
Year:
2001

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Abstract

We develop a probability model over image spaces and demonstrate its broad utility in mammographic imageanalysis. The model employs a pyramid representation to factor images across scale and a tree-structured set of hidden variables to capture long-range spatial dependencies. This factoring makes the computation of the density functions local and tractable. The result is a hierarchical mixture of conditional probabilities, similar to a hidden Markov model on a tree. The model parameters are found with maximum likelihood estimation using the EM algorithm. The utility of the model is demonstrated for three applications; 1) detection of mammographic masses in computer-aided diagnosis 2) qualitative assessment of model structure through mammographic synthesis and 3) compression of mammographicregions of interest.