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Conference Paper: Bayesian regularization of diffusion tensor images using hierarchical MCMC and loopy belief propagation

TitleBayesian regularization of diffusion tensor images using hierarchical MCMC and loopy belief propagation
Authors
KeywordsBayesian models
Diffusion tensor images
Image restoration
Markov chain Monte Carlo
Issue Date2010
PublisherIEEE. The Journal's web site is located at http://ieeexplore.ieee.org/xpl/conhome.jsp?punumber=1000349
Citation
The 17th IEEE International Conference on Image Processing (ICIP 2010), Hong Kong, China, 26-29 September 2010. In Proceedings of the 17th ICIP, 2010, p. 65-68 How to Cite?
AbstractBased on the theory of Markov Random Fields, a Bayesian regularization model for diffusion tensor images (DTI) is proposed in this paper. The low-degree parameterization of diffusion tensors in our model makes it less computationally intensive to obtain a maximum a posteriori (MAP) estimation. An approximate solution to the problem is achieved efficiently using hierarchical Markov Chain Monte Carlo (HMCMC), and a loopy belief propagation algorithm is applied to a coarse grid to obtain a good initial solution for hierarchical MCMC. Experiments on synthetic and real data demonstrate the effectiveness of our methods. © 2010 IEEE.
Persistent Identifierhttp://hdl.handle.net/10722/140001
ISSN
ISI Accession Number ID
References

 

DC FieldValueLanguage
dc.contributor.authorWei, Sen_HK
dc.contributor.authorHua, Jen_HK
dc.contributor.authorBu, Jen_HK
dc.contributor.authorChen, Cen_HK
dc.contributor.authorYu, Yen_HK
dc.date.accessioned2011-09-23T06:04:32Z-
dc.date.available2011-09-23T06:04:32Z-
dc.date.issued2010en_HK
dc.identifier.citationThe 17th IEEE International Conference on Image Processing (ICIP 2010), Hong Kong, China, 26-29 September 2010. In Proceedings of the 17th ICIP, 2010, p. 65-68en_HK
dc.identifier.issn1522-4880en_HK
dc.identifier.urihttp://hdl.handle.net/10722/140001-
dc.description.abstractBased on the theory of Markov Random Fields, a Bayesian regularization model for diffusion tensor images (DTI) is proposed in this paper. The low-degree parameterization of diffusion tensors in our model makes it less computationally intensive to obtain a maximum a posteriori (MAP) estimation. An approximate solution to the problem is achieved efficiently using hierarchical Markov Chain Monte Carlo (HMCMC), and a loopy belief propagation algorithm is applied to a coarse grid to obtain a good initial solution for hierarchical MCMC. Experiments on synthetic and real data demonstrate the effectiveness of our methods. © 2010 IEEE.en_HK
dc.languageengen_US
dc.publisherIEEE. The Journal's web site is located at http://ieeexplore.ieee.org/xpl/conhome.jsp?punumber=1000349en_HK
dc.relation.ispartofProceedings of the International Conference on Image Processing, ICIP 2010en_HK
dc.rightsInternational Conference on Image Processing Proceedings. Copyright © IEEE.-
dc.rightsCreative Commons: Attribution 3.0 Hong Kong License-
dc.rights©2010 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.-
dc.subjectBayesian modelsen_HK
dc.subjectDiffusion tensor imagesen_HK
dc.subjectImage restorationen_HK
dc.subjectMarkov chain Monte Carloen_HK
dc.titleBayesian regularization of diffusion tensor images using hierarchical MCMC and loopy belief propagationen_HK
dc.typeConference_Paperen_HK
dc.identifier.emailYu, Y:yzyu@cs.hku.hken_HK
dc.identifier.authorityYu, Y=rp01415en_HK
dc.description.naturepublished_or_final_version-
dc.identifier.doi10.1109/ICIP.2010.5651519en_HK
dc.identifier.scopuseid_2-s2.0-78651064818en_HK
dc.identifier.hkuros194322en_US
dc.relation.referenceshttp://www.scopus.com/mlt/select.url?eid=2-s2.0-78651064818&selection=ref&src=s&origin=recordpageen_HK
dc.identifier.spage65en_HK
dc.identifier.epage68en_HK
dc.identifier.isiWOS:000287728000016-
dc.publisher.placeUnited Statesen_HK
dc.description.otherThe 17th IEEE International Conference on Image Processing (ICIP 2010), Hong Kong, China, 26-29 September 2010. In Proceedings of the 17th ICIP, 2010, p. 65-68-
dc.identifier.scopusauthoridWei, S=36845050600en_HK
dc.identifier.scopusauthoridHua, J=7102121257en_HK
dc.identifier.scopusauthoridBu, J=7005200782en_HK
dc.identifier.scopusauthoridChen, C=35274602700en_HK
dc.identifier.scopusauthoridYu, Y=8554163500en_HK

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