Detection, synthesis and compression in mammographic image analysis with a hierarchical image probability model

Abstract

We develop a probability model over image spaces and demonstrate its broad utility in mammographic image analysis. 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 mammographic regions of interest.