Please use this identifier to cite or link to this item: https://scholarbank.nus.edu.sg/handle/10635/131608
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dc.titleExtraction of brain tumor from MR images using one-class support vector machine
dc.contributor.authorZhou, J.
dc.contributor.authorChan, K.L.
dc.contributor.authorChong, V.F.H.
dc.contributor.authorKrishnan, S.M.
dc.date.accessioned2016-11-29T01:20:40Z
dc.date.available2016-11-29T01:20:40Z
dc.date.issued2005
dc.identifier.citationZhou, J., Chan, K.L., Chong, V.F.H., Krishnan, S.M. (2005). Extraction of brain tumor from MR images using one-class support vector machine. Annual International Conference of the IEEE Engineering in Medicine and Biology - Proceedings 7 VOLS : 6411-6414. ScholarBank@NUS Repository.
dc.identifier.isbn0780387406
dc.identifier.issn05891019
dc.identifier.urihttp://scholarbank.nus.edu.sg/handle/10635/131608
dc.description.abstractA novel image segmentation approach by exploring one-class support vector machine (SVM) has been developed for the extraction of brain tumor from magnetic resonance (MR) images. Based on one-class SVM, the proposed method has the ability of learning the nonlinear distribution of the image data without prior knowledge, via the automatic procedure of SVM parameters training and an implicit learning kernel. After the learning process, the segmentation task is performed. The proposed technique is applied to 24 clinical MR images of brain tumor for both visual and quantitative evaluations. Experimental results suggest that the proposed query-based approach provides an effective and promising method for brain tumor extraction from MR images with high accuracy. © 2005 IEEE.
dc.sourceScopus
dc.subjectImage segmentation
dc.subjectMR image
dc.subjectSupport vector machine
dc.typeConference Paper
dc.contributor.departmentDIAGNOSTIC RADIOLOGY
dc.description.sourcetitleAnnual International Conference of the IEEE Engineering in Medicine and Biology - Proceedings
dc.description.volume7 VOLS
dc.description.page6411-6414
dc.description.codenCEMBA
dc.identifier.isiutNOT_IN_WOS
Appears in Collections:Staff Publications

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