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


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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Available from: Dongxaing Xu, May 07, 2014
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    • "In both [9] and [10], the model parameters are learnt separately for the BN and MRF (or CRF) parts. Moreover, the inference in [9] is performed sequentially, with inference results from the BN part fed into the the MRF part to perform further inference. The separate and sequential inference is not theoretically justified, and represents only an approximation to the simultaneous inference. "
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