Cited 7 times in
Geodesic Distance Algorithm for Extracting the Ascending Aorta from 3D CT Images
DC Field | Value | Language |
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dc.contributor.author | 장혁재 | - |
dc.contributor.author | 조익성 | - |
dc.date.accessioned | 2017-02-24T07:37:29Z | - |
dc.date.available | 2017-02-24T07:37:29Z | - |
dc.date.issued | 2016 | - |
dc.identifier.issn | 1748-670X | - |
dc.identifier.uri | https://ir.ymlib.yonsei.ac.kr/handle/22282913/146469 | - |
dc.description.abstract | This paper presents a method for the automatic 3D segmentation of the ascending aorta from coronary computed tomography angiography (CCTA). The segmentation is performed in three steps. First, the initial seed points are selected by minimizing a newly proposed energy function across the Hough circles. Second, the ascending aorta is segmented by geodesic distance transformation. Third, the seed points are effectively transferred through the next axial slice by a novel transfer function. Experiments are performed using a database composed of 10 patients' CCTA images. For the experiment, the ground truths are annotated manually on the axial image slices by a medical expert. A comparative evaluation with state-of-the-art commercial aorta segmentation algorithms shows that our approach is computationally more efficient and accurate under the DSC (Dice Similarity Coefficient) measurements. | - |
dc.description.statementOfResponsibility | open | - |
dc.format | application/pdf | - |
dc.language | English | - |
dc.publisher | Hindawi Publishing Corporation | - |
dc.relation.isPartOf | COMPUTATIONAL AND MATHEMATICAL METHODS IN MEDICINE | - |
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 | Algorithms | - |
dc.subject.MESH | Aorta/diagnostic imaging* | - |
dc.subject.MESH | Aorta/pathology | - |
dc.subject.MESH | Artifacts | - |
dc.subject.MESH | Computed Tomography Angiography* | - |
dc.subject.MESH | Databases, Factual | - |
dc.subject.MESH | False Positive Reactions | - |
dc.subject.MESH | Humans | - |
dc.subject.MESH | Image Processing, Computer-Assisted | - |
dc.subject.MESH | Imaging, Three-Dimensional* | - |
dc.subject.MESH | Models, Statistical | - |
dc.subject.MESH | Pattern Recognition, Automated | - |
dc.subject.MESH | Radiographic Image Interpretation, Computer-Assisted* | - |
dc.subject.MESH | Reproducibility of Results | - |
dc.subject.MESH | Risk Factors | - |
dc.subject.MESH | Tomography, X-Ray Computed* | - |
dc.title | Geodesic Distance Algorithm for Extracting the Ascending Aorta from 3D CT Images | - |
dc.type | Article | - |
dc.publisher.location | United States | - |
dc.contributor.college | College of Medicine | - |
dc.contributor.department | Dept. of Internal Medicine | - |
dc.contributor.googleauthor | Yeonggul Jang | - |
dc.contributor.googleauthor | Ho Yub Jung | - |
dc.contributor.googleauthor | Youngtaek Hong | - |
dc.contributor.googleauthor | Iksung Cho | - |
dc.contributor.googleauthor | Hackjoon Shim | - |
dc.contributor.googleauthor | Hyuk-Jae Chang | - |
dc.identifier.doi | 10.1155/2016/4561979 | - |
dc.contributor.localId | A03490 | - |
dc.contributor.localId | A03888 | - |
dc.relation.journalcode | J00634 | - |
dc.identifier.eissn | 1748-6718 | - |
dc.relation.journalsince | 2006~ | - |
dc.identifier.pmid | 26904151 | - |
dc.relation.journalbefore | ~2005 Journal of Theoretical Medicine | - |
dc.contributor.alternativeName | Chang, Hyuck Jae | - |
dc.contributor.alternativeName | Cho, Ik Sung | - |
dc.contributor.affiliatedAuthor | Chang, Hyuck Jae | - |
dc.contributor.affiliatedAuthor | Cho, Ik Sung | - |
dc.citation.volume | 2016 | - |
dc.citation.startPage | 4561979 | - |
dc.identifier.bibliographicCitation | COMPUTATIONAL AND MATHEMATICAL METHODS IN MEDICINE, Vol.2016 : 4561979, 2016 | - |
dc.date.modified | 2017-02-24 | - |
dc.identifier.rimsid | 45113 | - |
dc.type.rims | ART | - |
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