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dc.contributor.authorLiew, Alan Wee-Chungen_US
dc.contributor.authorYan, Hongen_US
dc.date.accessioned2017-04-24T12:52:32Z
dc.date.available2017-04-24T12:52:32Z
dc.date.issued2006en_US
dc.date.modified2009-10-16T05:17:52Z
dc.identifier.issn15734056en_US
dc.identifier.doihttp://www.bentham.org/cmir/contabs/cmir2-1.htm#8en_AU
dc.identifier.urihttp://hdl.handle.net/10072/24901
dc.description.abstractAccurate segmentation of magnetic resonance (MR) images of the brain is of interest in the study of many brain disorders. In this paper, we provide a review of some of the current approaches in the tissue segmentation of MR brain images. We broadly divided current MR brain image segmentation algorithms into three categories: classification-based, region-based, and contour-based, and discuss the advantages and disadvantages of these approaches. We also briefly review our recent work in this area. We show that by incorporating two key ideas into the conventional fuzzy cmeans clustering algorithm, we are able to take into account the local spatial context and compensate for the intensity nonuniformity (INU) artifact during the clustering process. We conclude this review by pointing to some possible future directions in this area.en_US
dc.description.peerreviewedYesen_US
dc.description.publicationstatusYesen_AU
dc.languageEnglishen_US
dc.language.isoen_AU
dc.publisherBentham Science Publishers Ltden_US
dc.publisher.placeThe Netherlandsen_US
dc.publisher.urihttp://www.bentham.org/cmir/index.htmen_AU
dc.relation.ispartofstudentpublicationNen_AU
dc.relation.ispartofpagefrom91en_US
dc.relation.ispartofpageto103en_US
dc.relation.ispartofissue1en_US
dc.relation.ispartofjournalCurrent Medical Imaging Reviewsen_US
dc.relation.ispartofvolume2en_US
dc.rights.retentionYen_AU
dc.subject.fieldofresearchcode280203en_US
dc.subject.fieldofresearchcode291599en_US
dc.titleCurrent Methods in the Automatic Tissue Segmentation of 3D Magnetic Resonance Brain Imagesen_US
dc.typeJournal articleen_US
dc.type.descriptionC1 - Peer Reviewed (HERDC)en_US
dc.type.codeC - Journal Articlesen_US
gro.date.issued2006
gro.hasfulltextNo Full Text


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