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dc.contributor.author문영식-
dc.date.accessioned2018-05-29T04:18:55Z-
dc.date.available2018-05-29T04:18:55Z-
dc.date.issued2017-01-
dc.identifier.citationOPTICAL ENGINEERING, v. 56, No. 1, Article no. 013106en_US
dc.identifier.issn0091-3286-
dc.identifier.issn1560-2303-
dc.identifier.urihttps://www.spiedigitallibrary.org/journals/Optical-Engineering/volume-56/issue-1/013106/Automatic-segmentation-of-coronary-arteries-from-computed-tomography-angiography-data/10.1117/1.OE.56.1.013106.full?SSO=1-
dc.identifier.urihttp://repository.hanyang.ac.kr/handle/20.500.11754/71605-
dc.description.abstractManual analysis of the bulk data generated by computed tomography angiography (CTA) is time consuming, and interpretation of such data requires previous knowledge and expertise of the radiologist. Therefore, an automatic method that can isolate the coronary arteries from a given CTA dataset is required. We present an automatic yet effective segmentation method to delineate the coronary arteries from a three-dimensional CTA data cloud. Instead of a region growing process, which is usually time consuming and prone to leakages, the method is based on the optimal thresholding, which is applied globally on the Hessian-based vesselness measure in a localized way (slice by slice) to track the coronaries carefully to their distal ends. Moreover, to make the process automatic, we detect the aorta using the Hough transform technique. The proposed segmentation method is independent of the starting point to initiate its process and is fast in the sense that coronary arteries are obtained without any preprocessing or postprocessing steps. We used 12 real clinical datasets to show the efficiency and accuracy of the presented method. Experimental results reveal that the proposed method achieves 95% average accuracy. (C) 2017 Society of Photo-Optical Instrumentation Engineers (SPIE)en_US
dc.language.isoen_USen_US
dc.publisherSPIE-SOC PHOTO-OPTICAL INSTRUMENTATION ENGINEERSen_US
dc.subjectcomputed tomography angiographyen_US
dc.subjectoptimal thresholdingen_US
dc.subjectHessian-based vesselnessen_US
dc.subjectsegmentationen_US
dc.subjectIMAGESen_US
dc.titleAutomatic segmentation of coronary arteries from computed tomography angiography data cloud using optimal thresholdingen_US
dc.typeArticleen_US
dc.relation.no1-
dc.relation.volume56-
dc.identifier.doi10.1117/1.OE.56.1.013106-
dc.relation.page1-8-
dc.relation.journalOPTICAL ENGINEERING-
dc.contributor.googleauthorAnsari, Muhammad Ahsan-
dc.contributor.googleauthorZai, Sammer-
dc.contributor.googleauthorMoon, Young Shik-
dc.relation.code2017002928-
dc.sector.campusE-
dc.sector.daehakCOLLEGE OF COMPUTING[E]-
dc.sector.departmentDIVISION OF COMPUTER SCIENCE-
dc.identifier.pidysmoon-
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COLLEGE OF COMPUTING[E] > COMPUTER SCIENCE(소프트웨어학부) > Articles
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