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Effective R2 map-based liver segmentation method in an MR image

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dc.contributor.authorEun, S.-J.-
dc.contributor.authorKwon, J.-
dc.contributor.authorKim, H.-
dc.contributor.authorWhangbo, T.-K.-
dc.date.available2020-02-29T09:44:28Z-
dc.date.created2020-02-11-
dc.date.issued2012-
dc.identifier.issn0000-0000-
dc.identifier.urihttps://scholarworks.bwise.kr/gachon/handle/2020.sw.gachon/17459-
dc.description.abstractObject recognition is usually processed based on region segmentation algorithm. Region segmentation in the IT field is carried out by computerized processing of various input information such as brightness, shape, and pattern analysis. If the information mentioned does not make sense, however, many limitations could occur with region segmentation during computer processing. Therefore, this paper suggests effective region segmentation method based on R2 information within the magnetic resonance (MR) theory. In this study, the experiment had been conducted using images including the liver region and by setting up feature points of R2 map as seed points for region growing to enable region segmentation even when the border line was not clear. As a result, an average area difference of 8.5%, which was higher than the accuracy of conventional region segmentation algorithm, was obtained. © 2012 IEEE.-
dc.language영어-
dc.language.isoen-
dc.relation.isPartOf2012 International Conference on Information Science and Applications, ICISA 2012-
dc.subjectComputer processing-
dc.subjectLiver regions-
dc.subjectLiver segmentation-
dc.subjectMR images-
dc.subjectPattern analysis-
dc.subjectRegion growing-
dc.subjectRegion segmentation-
dc.subjectSeed point-
dc.subjectTexture analysis-
dc.subjectD region-
dc.subjectImage texture-
dc.subjectInformation science-
dc.subjectMagnetic resonance-
dc.subjectObject recognition-
dc.subjectThree dimensional-
dc.subjectImage segmentation-
dc.titleEffective R2 map-based liver segmentation method in an MR image-
dc.typeArticle-
dc.type.rimsART-
dc.description.journalClass1-
dc.identifier.doi10.1109/ICISA.2012.6220957-
dc.identifier.bibliographicCitation2012 International Conference on Information Science and Applications, ICISA 2012-
dc.identifier.scopusid2-s2.0-84864202767-
dc.citation.title2012 International Conference on Information Science and Applications, ICISA 2012-
dc.contributor.affiliatedAuthorEun, S.-J.-
dc.contributor.affiliatedAuthorWhangbo, T.-K.-
dc.type.docTypeArticle-
dc.subject.keywordAuthor3D region growing-
dc.subject.keywordAuthorcomponent-
dc.subject.keywordAuthorMR image-
dc.subject.keywordAuthorLiver segmentation-
dc.subject.keywordAuthorR2 map-
dc.subject.keywordAuthorTexture analysis-
dc.subject.keywordPlusComputer processing-
dc.subject.keywordPlusLiver regions-
dc.subject.keywordPlusLiver segmentation-
dc.subject.keywordPlusMR images-
dc.subject.keywordPlusPattern analysis-
dc.subject.keywordPlusRegion growing-
dc.subject.keywordPlusRegion segmentation-
dc.subject.keywordPlusSeed point-
dc.subject.keywordPlusTexture analysis-
dc.subject.keywordPlusD region-
dc.subject.keywordPlusImage texture-
dc.subject.keywordPlusInformation science-
dc.subject.keywordPlusMagnetic resonance-
dc.subject.keywordPlusObject recognition-
dc.subject.keywordPlusThree dimensional-
dc.subject.keywordPlusImage segmentation-
dc.description.journalRegisteredClassscopus-
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Whangbo, Taeg Keun
College of IT Convergence (컴퓨터공학부(컴퓨터공학전공))
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