Mammographic mass detection with a hierarchical image probability (HIP) model

Clay D. Spence; Lucas Parra; Paul Sajda

doi:10.1117/12.387603

6 June 2000 Mammographic mass detection with a hierarchical image probability (HIP) model

Clay D. Spence, Lucas Parra, Paul Sajda

Proceedings Volume 3979, Medical Imaging 2000: Image Processing; (2000) https://doi.org/10.1117/12.387603
Event: Medical Imaging 2000, 2000, San Diego, CA, United States

Abstract

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 fix this, 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 masses in mammograms.

Citation Download Citation

Clay D. Spence, Lucas Parra, and Paul Sajda "Mammographic mass detection with a hierarchical image probability (HIP) model", Proc. SPIE 3979, Medical Imaging 2000: Image Processing, (6 June 2000); https://doi.org/10.1117/12.387603

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