A Novel Way of Incorporating Large-Scale Knowledge into MRF Prior Model

Authors:
Yang Chen;Wufan Chen;Pengcheng Shi;Yanqiu Feng;Qianjin Feng;Qingqi Wang;Zhiyong Huang
Affiliations:
Institute of Medical Information&Technology, School of Biomedical Engineering, Southern Medical University, Guangzhou, 510515, China and The 113 Hospital of People's Liberation Army, Ningbo, 31504 ...;Institute of Medical Information&Technology, School of Biomedical Engineering, Southern Medical University, Guangzhou, 510515, China;Institute of Medical Information&Technology, School of Biomedical Engineering, Southern Medical University, Guangzhou, 510515, China;Institute of Medical Information&Technology, School of Biomedical Engineering, Southern Medical University, Guangzhou, 510515, China;Institute of Medical Information&Technology, School of Biomedical Engineering, Southern Medical University, Guangzhou, 510515, China;The 113 Hospital of People's Liberation Army, Ningbo, 315040, China;The 113 Hospital of People's Liberation Army, Ningbo, 315040, China
Venue:
AIME '07 Proceedings of the 11th conference on Artificial Intelligence in Medicine
Year:
2007

Citing 2
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Markov random field modeling in image analysis

Markov random field modeling in image analysis
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IPMI '93 Proceedings of the 13th International Conference on Information Processing in Medical Imaging

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Abstract

Based on Markov Random Fields (MRF) theory, Bayesian methods have been accepted as an effective solution to overcome the ill-posed problems of image restoration and reconstruction. Traditionally, the knowledge in most of prior models is from a simply weighted differences between the pixel intensities within a small local neighborhood, so it can only provide limited prior information for regularization. Exploring the ways of incorporating more large-scale knowledge into prior model, this paper proposes an effective approach to incorporate large-scale image knowledge into MRF prior model. And a novel nonlocal prior is put forward. Relevant experiments in emission tomography prove that the proposed MRF nonlocal prior is capable of imposing more effective regularization on original reconstructions.