Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/1907
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dc.contributorDepartment of Electronic and Information Engineering-
dc.creatorWen, L-
dc.creatorBewley, M-
dc.creatorEberl, S-
dc.creatorFulham, MJ-
dc.creatorFeng, DD-
dc.date.accessioned2014-12-11T08:22:28Z-
dc.date.available2014-12-11T08:22:28Z-
dc.identifier.isbn978-1-4244-2003-2-
dc.identifier.urihttp://hdl.handle.net/10397/1907-
dc.language.isoenen_US
dc.publisherIEEEen_US
dc.rights© 2008 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.en_US
dc.rightsThis material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. In most cases, these works may not be reposted without the explicit permission of the copyright holder.en_US
dc.subjectDementiaen_US
dc.subjectData miningen_US
dc.subjectClassificationen_US
dc.subjectParametric imageen_US
dc.titleClassification of dementia from FDG-PET parametric images using data miningen_US
dc.typeConference Paperen_US
dc.description.otherinformationAuthor name used in this publication: Michael Fulhamen_US
dc.description.otherinformationAuthor name used in this publication: (David) Dagan Fengen_US
dc.description.otherinformationCentre for Multimedia Signal Processing, Department of Electronic and Information Engineeringen_US
dc.description.otherinformationRefereed conference paperen_US
dc.identifier.doi10.1109/ISBI.2008.4541020-
dcterms.abstractIt remains a challenge to identify the different types of dementia and separate these from various subtypes from the normal effects of ageing. In this paper the potential of parametric images from FDG-PET studies to aid the classification of dementia using data mining techniques was investigated. Scalar, joint, histogram and voxel-level features were used in the investigation with principal component analysis (PCA) for dimensionality reduction. The logistic regression model and the additive logistic regression model were applied in the classification. The results show that cerebral metabolic rate of glucose consumption (CMRGlc) was efficient in the classification of dementia and data mining using voxel-level features with PCA and the logistic regression model method achieving the best classification.-
dcterms.accessRightsopen accessen_US
dcterms.bibliographicCitation2008 5th IEEE International Symposium on Biomedical Imaging : from nano to macro : proceedings : May 14–17, 2008, Paris, France, p. 412-415-
dcterms.issued2008-
dc.identifier.scopus2-s2.0-51049117924-
dc.identifier.rosgroupidr38343-
dc.description.ros2007-2008 > Academic research: refereed > Refereed conference paper-
dc.description.oaVersion of Recorden_US
dc.identifier.FolderNumberOA_IR/PIRAen_US
dc.description.pubStatusPublisheden_US
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