Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/1909
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dc.contributorDepartment of Electronic and Information Engineering-
dc.creatorWong, KP-
dc.creatorFeng, DD-
dc.creatorMeikle, SR-
dc.creatorFulham, MJ-
dc.date.accessioned2014-12-11T08:22:28Z-
dc.date.available2014-12-11T08:22:28Z-
dc.identifier.isbn0-7803-6503-8-
dc.identifier.urihttp://hdl.handle.net/10397/1909-
dc.language.isoenen_US
dc.publisherIEEEen_US
dc.rights© 2001 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.subjectImage segmentationen_US
dc.subjectLungen_US
dc.subjectMedical image processingen_US
dc.subjectPositron emission tomographyen_US
dc.titleSegmentation of dynamic PET images using cluster analysisen_US
dc.typeConference Paperen_US
dc.description.otherinformationAuthor name used in this publication: Dagan Fengen_US
dc.identifier.doi10.1109/NSSMIC.2000.949251-
dcterms.abstractQuantitative PET studies can provide in-vivo measurements of dynamic physiological and biochemical processes in humans. A limitation of PET is its inability to provide precise anatomic localisation due to relatively poor spatial resolution when compared to MR imaging. Manual placement of regions of interest (ROIs) is commonly used in the clinical and research settings in analysis of PET datasets. However, this approach is operator dependent and time-consuming. Semi- or fully-automated ROI delineation (or segmentation) methods offer advantages by reducing operator error and subjectivity and thereby improving reproducibility. In this work, we describe an approach to automatically segment dynamic PET images using cluster analysis, and we validate our approach with a simulated phantom study and assess its performance in segmentation of dynamic lung data. Our preliminary results suggest that cluster analysis can be used to automatically segment tissues in dynamic PET studies and has the potential to replace manual ROI delineation.-
dcterms.accessRightsopen accessen_US
dcterms.bibliographicCitation2000 IEEE Nuclear Science Symposium conference record : October 15-20, 2000, Lyon, France, v. 3, p. 18/126 - 18/130-
dcterms.issued2001-
dc.identifier.scopus2-s2.0-003459417-
dc.description.oaVersion of Recorden_US
dc.identifier.FolderNumberOA_IR/PIRAen_US
dc.description.pubStatusPublisheden_US
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