Tagged: Hidden Markov models

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

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.

Hierarchical image probability (HIP) models

We formulate a model for probability distributions on image spaces. We show that any distribution of images can be factored exactly into conditional distributions of feature vectors at one resolution (pyramid level) conditioned on the image information at lower resolutions. We would like to factor this over positions in the pyramid levels to make it tractable, but such factoring may miss long-range dependencies. To capture long-range dependencies, we introduce hidden class labels at each pixel in the pyramid. The result is a hierarchical mixture of conditional probabilities, similar to a hidden Markov model on a tree. The model parameters can be found with maximum likelihood estimation using the EM algorithm. We have obtained encouraging preliminary results on the problems of detecting various objects in SAR images and target recognition in optical aerial images.