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Independent component analysis를 이용한 의료영상의 자동분할에 관한 연구

DC Field Value Language
dc.contributor.author김남현-
dc.contributor.author유선국-
dc.date.accessioned2015-07-15T16:47:24Z-
dc.date.available2015-07-15T16:47:24Z-
dc.date.issued2003-
dc.identifier.issn1229-6287-
dc.identifier.urihttps://ir.ymlib.yonsei.ac.kr/handle/22282913/113603-
dc.description.abstractMedical image segmentation is the process by which an original image is partitioned into some homogeneous regions like bones, soft tissues, etc. This study demonstrates an automatic medical image segmentation technique based on independent component analysis. Independent component analysis is a generalization of principal component analysis which encodes the higher-order dependencies in the input in addition to the correlations. It extracts statistically independent components from input data. Use of automatic medical image segmentation technique using independent component analysis under the assumption that medical image consists of some statistically independent parts leads to a method that allows for more accurate segmentation of bones from CT data. The result of automatic segmentation using independent component analysis with square test data was evaluated using probability of error(PE) and ultimate measurement accuracy(UMA) value. It was also compared to a general segmentation method using threshold based on sensitivity(True Positive Rate), specificity(False Positive Rate) and mislabelling rate. The evaluation result was done statistical Paired-t test. Most of the results show that the automatic segmentation using independent component analysis has better result than general segmentation using threshold.-
dc.description.statementOfResponsibilityopen-
dc.format.extent64~75-
dc.relation.isPartOf전기학회논문지 D-
dc.rightsCC BY-NC-ND 2.0 KR-
dc.rights.urihttps://creativecommons.org/licenses/by-nc-nd/2.0/kr/-
dc.subject.MESHsegmentation-
dc.subject.MESHindependent component analysis-
dc.subject.MESHmedical image-
dc.subject.MESHstatisically independent component-
dc.titleIndependent component analysis를 이용한 의료영상의 자동분할에 관한 연구-
dc.title.alternativeA Study of Automatic Medical Image Segmentation using Independent Component Analysis-
dc.typeArticle-
dc.contributor.collegeCollege of Medicine (의과대학)-
dc.contributor.departmentDept. of Medical Engineering (의학공학)-
dc.contributor.googleauthor배수현-
dc.contributor.googleauthor김남현-
dc.contributor.googleauthor김남형-
dc.contributor.googleauthor유선국-
dc.identifier.doiOAK-2003-00443-
dc.admin.authorfalse-
dc.admin.mappingfalse-
dc.contributor.localIdA02471-
dc.contributor.localIdA00357-
dc.relation.journalcodeJ03252-
dc.subject.keywordsegmentation-
dc.subject.keywordindependent component analysis-
dc.subject.keywordmedical image-
dc.subject.keywordstatisically independent component-
dc.contributor.alternativeNameKim, Nam Hyun-
dc.contributor.alternativeNameYoo, Sun Kook-
dc.contributor.affiliatedAuthorYoo, Sun Kook-
dc.contributor.affiliatedAuthorKim, Nam Hyun-
dc.rights.accessRightsfree-
dc.citation.volume52-
dc.citation.number1-
dc.citation.startPage64-
dc.citation.endPage75-
dc.identifier.bibliographicCitation전기학회논문지 D, Vol.52(1) : 64-75, 2003-
dc.identifier.rimsid44442-
dc.type.rimsART-
Appears in Collections:
1. College of Medicine (의과대학) > Dept. of Medical Engineering (의학공학교실) > 1. Journal Papers

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