Cited 16 times in
A fast seed detection using local geometrical feature for automatic tracking of coronary arteries in CTA.
DC Field | Value | Language |
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dc.contributor.author | 심학준 | - |
dc.contributor.author | 장혁재 | - |
dc.contributor.author | 전병환 | - |
dc.contributor.author | 한동진 | - |
dc.contributor.author | 홍영택 | - |
dc.date.accessioned | 2015-01-06T17:35:52Z | - |
dc.date.available | 2015-01-06T17:35:52Z | - |
dc.date.issued | 2014 | - |
dc.identifier.issn | 0169-2607 | - |
dc.identifier.uri | https://ir.ymlib.yonsei.ac.kr/handle/22282913/100277 | - |
dc.description.abstract | We 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.statementOfResponsibility | open | - |
dc.relation.isPartOf | COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE | - |
dc.rights | CC BY-NC-ND 2.0 KR | - |
dc.rights.uri | https://creativecommons.org/licenses/by-nc-nd/2.0/kr/ | - |
dc.subject.MESH | Artificial Intelligence* | - |
dc.subject.MESH | Computer Systems | - |
dc.subject.MESH | Coronary Angiography/methods* | - |
dc.subject.MESH | Coronary Artery Disease/diagnostic imaging* | - |
dc.subject.MESH | Coronary Vessels/diagnostic imaging* | - |
dc.subject.MESH | Humans | - |
dc.subject.MESH | Pattern Recognition, Automated/methods* | - |
dc.subject.MESH | Radiographic Image Enhancement/methods* | - |
dc.subject.MESH | Radiographic Image Interpretation, Computer-Assisted/methods | - |
dc.subject.MESH | Reproducibility of Results | - |
dc.subject.MESH | Sensitivity and Specificity | - |
dc.subject.MESH | Tomography, X-Ray Computed/methods* | - |
dc.title | A fast seed detection using local geometrical feature for automatic tracking of coronary arteries in CTA. | - |
dc.type | Article | - |
dc.contributor.college | College of Medicine (의과대학) | - |
dc.contributor.department | Yonsei Biomedical Research Center (연세의생명연구원) | - |
dc.contributor.googleauthor | Dongjin Han | - |
dc.contributor.googleauthor | Nam-Thai Doan | - |
dc.contributor.googleauthor | Hackjoon Shim | - |
dc.contributor.googleauthor | Byunghwan Jeon | - |
dc.contributor.googleauthor | Hyunna Lee | - |
dc.contributor.googleauthor | Youngtaek Hong | - |
dc.contributor.googleauthor | Hyuk-Jae Chang | - |
dc.identifier.doi | 10.1016/j.cmpb.2014.07.005 | - |
dc.admin.author | false | - |
dc.admin.mapping | false | - |
dc.contributor.localId | A02215 | - |
dc.contributor.localId | A03490 | - |
dc.contributor.localId | A03514 | - |
dc.contributor.localId | A04276 | - |
dc.contributor.localId | A04418 | - |
dc.contributor.localId | A03296 | - |
dc.relation.journalcode | J00637 | - |
dc.identifier.eissn | 1872-7565 | - |
dc.identifier.pmid | 25106730 | - |
dc.identifier.url | http://www.sciencedirect.com/science/article/pii/S0169260714002934 | - |
dc.subject.keyword | Centerline tracking | - |
dc.subject.keyword | Coronary artery segmentation | - |
dc.subject.keyword | Coronary computed tomographic angiography (CCTA) | - |
dc.subject.keyword | ROC curve | - |
dc.subject.keyword | Seed detection | - |
dc.contributor.alternativeName | Shim, Hack Joon | - |
dc.contributor.alternativeName | Lee, Hyun Jung | - |
dc.contributor.alternativeName | Chang, Hyuck Jae | - |
dc.contributor.alternativeName | Jeon, Byung Hwan | - |
dc.contributor.alternativeName | Han, Dong Jin | - |
dc.contributor.alternativeName | Hong, Young Taek | - |
dc.contributor.affiliatedAuthor | Shim, Hack Joon | - |
dc.contributor.affiliatedAuthor | Chang, Hyuck Jae | - |
dc.contributor.affiliatedAuthor | Jeon, Byung Hwan | - |
dc.contributor.affiliatedAuthor | Han, Dong Jin | - |
dc.contributor.affiliatedAuthor | Hong, Young Taek | - |
dc.contributor.affiliatedAuthor | Lee, Hyun Jung | - |
dc.rights.accessRights | free | - |
dc.citation.volume | 117 | - |
dc.citation.number | 2 | - |
dc.citation.startPage | 179 | - |
dc.citation.endPage | 188 | - |
dc.identifier.bibliographicCitation | COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE, Vol.117(2) : 179-188, 2014 | - |
dc.identifier.rimsid | 57563 | - |
dc.type.rims | ART | - |
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