Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/1903
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dc.contributorDepartment of Electronic and Information Engineering-
dc.creatorXia, Y-
dc.creatorWen, L-
dc.creatorEberl, S-
dc.creatorFulham, MJ-
dc.creatorFeng, DD-
dc.date.accessioned2014-12-11T08:26:44Z-
dc.date.available2014-12-11T08:26:44Z-
dc.identifier.isbn978-1-4244-1815-2-
dc.identifier.urihttp://hdl.handle.net/10397/1903-
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.subjectAlgorithmsen_US
dc.subjectAlzheimer Diseaseen_US
dc.subjectBrainen_US
dc.subjectDementiaen_US
dc.subjectDiagnosis, Differentialen_US
dc.subjectFluorodeoxyglucose F18en_US
dc.subjectHumansen_US
dc.subjectImage Enhancementen_US
dc.subjectImage Interpretation, Computer-Assisteden_US
dc.subjectPositron-Emission Tomographyen_US
dc.subjectPrincipal Component Analysisen_US
dc.subjectRadiopharmaceuticalsen_US
dc.subjectReproducibility of Resultsen_US
dc.subjectSensitivity and Specificityen_US
dc.titleGenetic algorithm-based PCA eigenvector selection and weighting for automated identification of dementia using FDG-PET imagingen_US
dc.typeConference Paperen_US
dc.description.otherinformationAuthor name used in this publication: Michael Fulhamen_US
dc.description.otherinformationAuthor name used in this publication: Dagan Fengen_US
dc.description.otherinformationRefereed conference paperen_US
dc.identifier.doi10.1109/IEMBS.2008.4650290-
dcterms.abstractParametric FDG-PET data offer the potential for an automated identification of the different dementia syndromes. Principal component analysis (PCA) can be used for feature extraction in FDG-PET. However, standard PCA is not always successful in delineating the features that have the best discrimination ability. We report a genetic algorithm-based method to identify an optimal combination of eigenvectors so that the resultant features are capable of successfully separating patients with suspected Alzheimer's disease and frontotemporal dementia from normal controls. We compared our approach with standard PCA on a set of 210 clinical cases and improved the performance in separating the dementia types with an accuracy of 90.0% and a Kappa statistic of 0.849. There was very good agreement between the automated technique and the diagnosis given by clinicians.-
dcterms.accessRightsopen accessen_US
dcterms.bibliographicCitationProceedings of the 30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society : personalized healthcare through technology : August 20-24, 2008, Vancouver, British Columbia, Canada, p. 4812-4815-
dcterms.issued2008-
dc.identifier.scopus2-s2.0-61849113811-
dc.relation.ispartofbookProceedings of the 30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society : personalized healthcare through technology : August 20-24, 2008, Vancouver, British Columbia, Canada-
dc.relation.conferenceIEEE Engineering in Medicine and Biology Society. Conference [EMBC]-
dc.identifier.rosgroupidr44923-
dc.description.ros2008-2009 > 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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