Conference Paper

Image segmentation based on Bayesian network-Markov random field model and its application to in vivo plaque composition.

Dept. of Radiol., Washington Univ., Seattle, WA
DOI: 10.1109/ISBI.2006.1624872 Conference: Proceedings of the 2006 IEEE International Symposium on Biomedical Imaging: From Nano to Macro, Arlington, VA, USA, 6-9 April 2006
Source: DBLP

ABSTRACT Combining Bayesian network (BN) and Markov random field (MRF) models, this paper presents an effective supervised image segmentation algorithm. Representing information from different features, a Bayesian network generates the probability map for each pixel via the conditional PDF (probability density function) learned from a limited training data set. Considering the spatial relation and a priori knowledge of the image, MRF theory is used to generate a reasonable segmentation by minimizing the proposed energy functional. Applying this algorithm to multi-contrast MR image in vivo plaque composition measurement shows comparable results with expert manual segmentation

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