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A fast seed detection using local geometrical feature for automatic tracking of coronary arteries in CTA.

DC FieldValueLanguage
dc.contributor.author심학준-
dc.contributor.author장혁재-
dc.contributor.author전병환-
dc.contributor.author한동진-
dc.contributor.author홍영택-
dc.date.accessioned2015-01-06T17:35:52Z-
dc.date.available2015-01-06T17:35:52Z-
dc.date.issued2014-
dc.identifier.issn0169-2607-
dc.identifier.urihttps://ir.ymlib.yonsei.ac.kr/handle/22282913/100277-
dc.description.abstractWe propose a fast seed detection for automatic tracking of coronary arteries in coronary computed tomographic angiography (CCTA). To detect vessel regions, Hessian-based filtering is combined with a new local geometric feature that is based on the similarity of the consecutive cross-sections perpendicular to the vessel direction. It is in turn founded on the prior knowledge that a vessel segment is shaped like a cylinder in axial slices. To improve computational efficiency, an axial slice, which contains part of three main coronary arteries, is selected and regions of interest (ROIs) are extracted in the slice. Only for the voxels belonging to the ROIs, the proposed geometric feature is calculated. With the seed points, which are the centroids of the detected vessel regions, and their vessel directions, vessel tracking method can be used for artery extraction. Here a particle filtering-based tracking algorithm is tested. Using 19 clinical CCTA datasets, it is demonstrated that the proposed method detects seed points and can be used for full automatic coronary artery extraction. ROC (receiver operating characteristic) curve analysis shows the advantages of the proposed method.-
dc.description.statementOfResponsibilityopen-
dc.relation.isPartOfCOMPUTER METHODS AND PROGRAMS IN BIOMEDICINE-
dc.rightsCC BY-NC-ND 2.0 KR-
dc.rights.urihttps://creativecommons.org/licenses/by-nc-nd/2.0/kr/-
dc.subject.MESHArtificial Intelligence*-
dc.subject.MESHComputer Systems-
dc.subject.MESHCoronary Angiography/methods*-
dc.subject.MESHCoronary Artery Disease/diagnostic imaging*-
dc.subject.MESHCoronary Vessels/diagnostic imaging*-
dc.subject.MESHHumans-
dc.subject.MESHPattern Recognition, Automated/methods*-
dc.subject.MESHRadiographic Image Enhancement/methods*-
dc.subject.MESHRadiographic Image Interpretation, Computer-Assisted/methods-
dc.subject.MESHReproducibility of Results-
dc.subject.MESHSensitivity and Specificity-
dc.subject.MESHTomography, X-Ray Computed/methods*-
dc.titleA fast seed detection using local geometrical feature for automatic tracking of coronary arteries in CTA.-
dc.typeArticle-
dc.contributor.collegeCollege of Medicine (의과대학)-
dc.contributor.departmentYonsei Biomedical Research Center (연세의생명연구원)-
dc.contributor.googleauthorDongjin Han-
dc.contributor.googleauthorNam-Thai Doan-
dc.contributor.googleauthorHackjoon Shim-
dc.contributor.googleauthorByunghwan Jeon-
dc.contributor.googleauthorHyunna Lee-
dc.contributor.googleauthorYoungtaek Hong-
dc.contributor.googleauthorHyuk-Jae Chang-
dc.identifier.doi10.1016/j.cmpb.2014.07.005-
dc.admin.authorfalse-
dc.admin.mappingfalse-
dc.contributor.localIdA02215-
dc.contributor.localIdA03490-
dc.contributor.localIdA03514-
dc.contributor.localIdA04276-
dc.contributor.localIdA04418-
dc.contributor.localIdA03296-
dc.relation.journalcodeJ00637-
dc.identifier.eissn1872-7565-
dc.identifier.pmid25106730-
dc.identifier.urlhttp://www.sciencedirect.com/science/article/pii/S0169260714002934-
dc.subject.keywordCenterline tracking-
dc.subject.keywordCoronary artery segmentation-
dc.subject.keywordCoronary computed tomographic angiography (CCTA)-
dc.subject.keywordROC curve-
dc.subject.keywordSeed detection-
dc.contributor.alternativeNameShim, Hack Joon-
dc.contributor.alternativeNameLee, Hyun Jung-
dc.contributor.alternativeNameChang, Hyuck Jae-
dc.contributor.alternativeNameJeon, Byung Hwan-
dc.contributor.alternativeNameHan, Dong Jin-
dc.contributor.alternativeNameHong, Young Taek-
dc.contributor.affiliatedAuthorShim, Hack Joon-
dc.contributor.affiliatedAuthorChang, Hyuck Jae-
dc.contributor.affiliatedAuthorJeon, Byung Hwan-
dc.contributor.affiliatedAuthorHan, Dong Jin-
dc.contributor.affiliatedAuthorHong, Young Taek-
dc.contributor.affiliatedAuthorLee, Hyun Jung-
dc.rights.accessRightsfree-
dc.citation.volume117-
dc.citation.number2-
dc.citation.startPage179-
dc.citation.endPage188-
dc.identifier.bibliographicCitationCOMPUTER METHODS AND PROGRAMS IN BIOMEDICINE, Vol.117(2) : 179-188, 2014-
Appears in Collections:
1. College of Medicine (의과대학) > Yonsei Biomedical Research Center (연세의생명연구원) > 1. Journal Papers
1. College of Medicine (의과대학) > Dept. of Internal Medicine (내과학교실) > 1. Journal Papers
5. Research Institutes (연구소) > Yonsei Cardiovascular Research Institute (심혈관연구소) > 1. Journal Papers
1. College of Medicine (의과대학) > Dept. of Ophthalmology (안과학교실) > 1. Journal Papers

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