Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/1873
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dc.contributorDepartment of Electronic and Information Engineering-
dc.creatorParker, BJ-
dc.creatorFeng, DD-
dc.date.accessioned2014-12-11T08:25:35Z-
dc.date.available2014-12-11T08:25:35Z-
dc.identifier.issn0018-9499-
dc.identifier.urihttp://hdl.handle.net/10397/1873-
dc.language.isoenen_US
dc.publisherInstitute of Electrical and Electronics Engineersen_US
dc.rights© 2005 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.subjectBrain modelingen_US
dc.subjectCovariance analysisen_US
dc.subjectGraph theoryen_US
dc.subjectImage segmentationen_US
dc.subjectMumford–Shahen_US
dc.subjectPattern recognitionen_US
dc.subjectPositron emission tomographyen_US
dc.titleGraph-based Mumford-Shah segmentation of dynamic PET with application to input function estimationen_US
dc.typeJournal/Magazine Articleen_US
dc.description.otherinformationAuthor name used in this publication: (David) Dagan Fengen_US
dc.identifier.spage79-
dc.identifier.epage89-
dc.identifier.volume52-
dc.identifier.issue1-
dc.identifier.doi10.1109/TNS.2004.843133-
dcterms.abstractA graph-theoretic three-dimensional (3-D) segmentation algorithm based on Mumford-Shah energy minimization is applied to the segmentation of brain [sup 18]F-fluoro-deoxyglucose (FDG) dynamic positron emission tomography data for the automated extraction of tissues with distinct time activity curves (TACs), and, in particular, extraction of the internal carotid arteries and venous sinuses for the noninvasive estimation of the input arterial TAC. Preprocessing by principal component analysis (PCA) and a Mahalanobis distance metric provide segmentation based on distinct TAC shape rather than simply activity levels. Evaluations on simulation and clinical FDG brain positron emission tomography (PET) studies demonstrate that differing tissue types can be accurately demarcated with superior performance to k-means clustering approaches, and, in particular, the internal carotids and venous sinuses can be robustly segmented in clinical brain dynamic PET datasets, allowing for the fully automatic noninvasive estimation of the arterial input curve.-
dcterms.accessRightsopen accessen_US
dcterms.bibliographicCitationIEEE transactions on nuclear science, Feb. 2005, v. 52, no. 1, p. 79-89-
dcterms.isPartOfIEEE transactions on nuclear science-
dcterms.issued2005-02-
dc.identifier.isiWOS:000228168700013-
dc.identifier.scopus2-s2.0-17644373788-
dc.identifier.eissn1558-1578-
dc.identifier.rosgroupidr22156-
dc.description.ros2004-2005 > Academic research: refereed > Publication in refereed journal-
dc.description.oaVersion of Recorden_US
dc.identifier.FolderNumberOA_IR/PIRAen_US
dc.description.pubStatusPublisheden_US
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