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Proceedings Paper

Mammographic mass detection with a hierarchical image probability (HIP) model
Author(s): Clay D. Spence; Lucas Parra; Paul Sajda
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Paper 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.

Paper Details

Date Published: 6 June 2000
PDF: 8 pages
Proc. SPIE 3979, Medical Imaging 2000: Image Processing, (6 June 2000); doi: 10.1117/12.387603
Show Author Affiliations
Clay D. Spence, Sarnoff Corp. (United States)
Lucas Parra, Sarnoff Corp. (United States)
Paul Sajda, Sarnoff Corp. (United States)


Published in SPIE Proceedings Vol. 3979:
Medical Imaging 2000: Image Processing
Kenneth M. Hanson, Editor(s)

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