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dc.contributor.authorOppedal, Ketil
dc.contributor.authorEftestøl, Trygve
dc.contributor.authorEngan, Kjersti
dc.contributor.authorBeyer, Mona K.
dc.contributor.authorAarsland, Dag
dc.date.accessioned2015-05-12T07:11:57Z
dc.date.available2015-05-12T07:11:57Z
dc.date.issued2015
dc.identifier.citationOppedal, K., Eftestøl, T., Engan, K., Beyer, Mona K., and Aarsland, D. (2015) Classifying Dementia Using Local Binary Patterns from Different Regions in Magnetic Resonance Images, International Journal of Biomedical Imaging, vol. 2015, Article ID 572567nb_NO
dc.identifier.urihttp://hdl.handle.net/11250/283526
dc.descriptionhttp://www.hindawi.com/journals/ijbi/2015/572567/abs/
dc.description.abstractDementia is an evolving challenge in society, and no disease-modifying treatment exists. Diagnosis can be demanding and MR imaging may aid as a noninvasive method to increase prediction accuracy. We explored the use of 2D local binary pattern (LBP) extracted from FLAIR and T1 MR images of the brain combined with a Random Forest classifier in an attempt to discern patients with Alzheimer’s disease (AD), Lewy body dementia (LBD), and normal controls (NC). Analysis was conducted in areas with white matter lesions (WML) and all of white matter (WM). Results from 10-fold nested cross validation are reported as mean accuracy, precision, and recall with standard deviation in brackets.The best result we achieved was in the two-class problem NC versus AD + LBD with total accuracy of 0.98 (0.04). In the three-class problem AD versus LBD versus NC and the two-class problem AD versus LBD, we achieved 0.87 (0.08) and 0.74 (0.16), respectively. The performance using 3DT1 images was notably better than when using FLAIR images.Theresults fromtheWMregion gave similar results as in theWMLregion.Our study demonstrates that LBP texture analysis in brain MR images can be successfully used for computer based dementia diagnosis.nb_NO
dc.language.isoengnb_NO
dc.publisherHindawi Publishing Corporationnb_NO
dc.rightsNavngivelse 3.0 Norge*
dc.rights.urihttp://creativecommons.org/licenses/by/3.0/no/*
dc.subjectAlzheimernb_NO
dc.subjectAlzheimer'snb_NO
dc.subjectdementianb_NO
dc.subjectmagnetic resonance imagesnb_NO
dc.subjectLewy body dementianb_NO
dc.subjectcomputer based diagnosisnb_NO
dc.subjectAlzheimers
dc.titleClassifying dementia using local binary patterns from different regions in magnetic resonance imagesnb_NO
dc.typeJournal articlenb_NO
dc.typePeer reviewednb_NO
dc.subject.nsiVDP::Medical disciplines: 700::Clinical medical disciplines: 750nb_NO
dc.subject.nsiVDP::Technology: 500::Information and communication technology: 550nb_NO
dc.source.volume2015nb_NO
dc.source.journalInternational Journal of Biomedical Imagingnb_NO
dc.identifier.doi10.1155/2015/572567


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