Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/1891
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dc.contributorDepartment of Electronic and Information Engineering-
dc.creatorChen, S-
dc.creatorWong, LK-
dc.creatorFeng, DD-
dc.date.accessioned2014-12-11T08:26:44Z-
dc.date.available2014-12-11T08:26:44Z-
dc.identifier.isbn0-7803-7750-8-
dc.identifier.urihttp://hdl.handle.net/10397/1891-
dc.language.isoenen_US
dc.publisherIEEEen_US
dc.rights© 2003 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.subjectCluster analysisen_US
dc.subjectSegmentationen_US
dc.subjectHepatocellular Carcinoma (HCC)en_US
dc.subjectPositron Emission Tomography (PET)en_US
dc.subjectParameter Ken_US
dc.titleA new automatic detection approach for hepatocellular carcinoma using ¹¹C-acetate positron emission tomographyen_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/ICIP.2003.1247150-
dcterms.abstractFunctional imaging techniques such as Positron Emission Tomography (PET) has the potential for early diagnosis of malignant tumors. However, 40-50% of Hepatocellular Carcinoma (HCC), a common malignancy worldwide, can hardly be detected by the widely used [sup 18]F-2-fluoro-2-deoxy-D-glucose (FDG) PET. ¹¹C-acetate PET has recently been found effective for detecting HCC. To perform quantitative analysis to obtain the diagnosis information, regions of interest (ROls) are needed to be extracted. Manual placement of ROIs is subject to operator's skill and time-consuming. Furthermore, the small sizes of some ROIs make the task even more difficult. In this paper, we propose an approach to segment the dynamic ¹¹C-acetate PET liver images automatically. The curves extracted from some segmented ROIs are then fitted to the presented ¹¹C-acetate liver model. Finally, the parameter K, which has been validated as an indicator for detecting HCC, can be calculated.-
dcterms.accessRightsopen accessen_US
dcterms.bibliographicCitationICIP 2003 : 2003 International Conference on Image Processing : proceedings : September 14-17, 2003, Barcelona, Spain, v. 1, p. 1065-1068-
dcterms.issued2003-
dc.identifier.isiWOS:000187010000267-
dc.identifier.scopus2-s2.0-0344666517-
dc.relation.ispartofbookICIP 2003 : 2003 International Conference on Image Processing : proceedings : September 14-17, 2003, Barcelona, Spain-
dc.relation.conferenceIEEE International Conference on Image Processing [ICIP]-
dc.identifier.rosgroupidr18452-
dc.description.ros2003-2004 > 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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