Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/1889
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dc.contributorDepartment of Electronic and Information Engineering-
dc.creatorCai, W-
dc.creatorFeng, DD-
dc.creatorFulton, R-
dc.date.accessioned2014-12-11T08:26:45Z-
dc.date.available2014-12-11T08:26:45Z-
dc.identifier.isbn0-7803-6503-8-
dc.identifier.urihttp://hdl.handle.net/10397/1889-
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.subjectKnowledge based systemsen_US
dc.subjectMedical image processingen_US
dc.subjectNoiseen_US
dc.subjectPositron emission tomographyen_US
dc.subjectSmoothing methodsen_US
dc.titleA knowledge-based image smoothing technique for dynamic PET studiesen_US
dc.typeConference Paperen_US
dc.description.otherinformationAuthor name used in this publication: 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/NSSMIC.2000.949248-
dcterms.abstractMany techniques have been proposed to reduce image noise in dynamic positron emission tomography (PET) imaging. However, these smoothing methods are usually based on the spatial domain and local statistical properties. Smoothing algorithms specifically designed for dynamic image data have not previously been investigated in detail. We present a knowledge-based smoothing technique that aims to diminish the noise and improve the quality of the dynamic images. By taking advantage of domain specific physiological kinetic knowledge, this technique can provide dynamic images with high noise reduction while preserving edges and subtle details.-
dcterms.accessRightsopen accessen_US
dcterms.bibliographicCitation2000 IEEE Nuclear Science Symposium conference record : October 15-20, 2000, Lyon, France, v. 3, p. 18/114 - 18/117-
dcterms.issued2001-
dc.identifier.scopus2-s2.0-0034593542-
dc.identifier.rosgroupidr02333-
dc.description.ros2000-2001 > 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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