Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/1906
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dc.contributorDepartment of Electronic and Information Engineering-
dc.creatorWen, L-
dc.creatorEberl, S-
dc.creatorFulham, MJ-
dc.creatorFeng, DD-
dc.creatorBai, J-
dc.date.accessioned2014-12-11T08:22:28Z-
dc.date.available2014-12-11T08:22:28Z-
dc.identifier.isbn978-1-4244-2295-1-
dc.identifier.urihttp://hdl.handle.net/10397/1906-
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.subjectMonte Carlo methodsen_US
dc.subjectBiological tissuesen_US
dc.subjectCurve fittingen_US
dc.subjectFuzzy set theoryen_US
dc.subjectLeast squares approximationsen_US
dc.subjectMedical image processingen_US
dc.subjectParameter estimationen_US
dc.subjectPattern clusteringen_US
dc.subjectSingle photon emission computed tomographyen_US
dc.titleAdaptive fuzzy clustering in constructing parametric images for low SNR functional 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/MMSP.2008.4665059-
dcterms.abstractFunctional imaging can provide quantitative functional parameters to aid early diagnosis. Low signal to noise ratio (SNR) in functional imaging, especially for single photon emission computed tomography, poses a challenge in generating voxel-wise parametric images due to unreliable or physiologically meaningless parameter estimates. Our aim was to systematically investigate the performance of our recently proposed adaptive fuzzy clustering (AFC) technique, which applies standard fuzzy clustering to sub-divided data. Monte Carlo simulations were performed to generate noisy dynamic SPECT data with quantitative analysis for the fitting using the general linear least square method (GLLS) and enhanced model-aided GLLS methods. The results show that AFC substantially improves computational efficiency and obtains improved reliability as standard fuzzy clustering in estimating parametric images but is prone to slight underestimation. Normalization of tissue time activity curves may lead to severe overestimation for small structures when AFC is applied.-
dcterms.accessRightsopen accessen_US
dcterms.bibliographicCitationProceedings of the 2008 IEEE 10th Workshop on Multimedia Signal Processing : 8-10 October, 2008, Cairns, Australia, p. 117-121-
dcterms.issued2008-
dc.identifier.scopus2-s2.0-58049090876-
dc.identifier.rosgroupidr43456-
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
Appears in Collections:Conference Paper
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