A multi-scale probabilistic network model for detection, synthesis and compression in mammographic image analysis.
We develop a probabilistic network model over image spaces and demonstrate its broad utility in mammographic image analysis, particularly with respect to computer-aided diagnosis. The model employs a multi-scale pyramid decomposition to factor images across scale and a network of tree-structured 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 expectation-maximization algorithm. The utility of the model is demonstrated for three applications: (1) detection of mammographic masses for computer-aided diagnosis; (2) qualitative assessment of model structure through mammographic synthesis; and (3) compression of mammographic regions of interest.
Sajda
Paul
P
Department of Biomedical Engineering, Columbia University, 351 Engineering Terrace, MC 8904, New York, NY 10027, USA. sajda@columbia.edu
Spence
Clay
C
Parra
Lucas
L
D005260:Female; D006801:Humans; D007091:Image Processing, Computer-Assisted; D008327:Mammography; D015233:Models, Statistical; D016571:Neural Networks, Computer; D011857:Radiographic Image Interpretation, Computer-Assisted