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Segmentation and Rigid Registration of Liver Dynamic Computed Tomography Images for Diagnostic Assessment of Fatty Liver Disease

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dc.contributor.author이선영-
dc.date.accessioned2023-11-28T03:29:03Z-
dc.date.available2023-11-28T03:29:03Z-
dc.date.issued2023-09-
dc.identifier.urihttps://ir.ymlib.yonsei.ac.kr/handle/22282913/196823-
dc.description.abstractThis study presents a method for diagnosing fatty liver disease by using time-difference liver computed tomography (CT) images of the same patient to perform segmentation and rigid registration on liver regions, excluding the vascular regions. The proposed method comprises three main steps. First, the liver region is segmented in the precontrast phase, and the liver and liver vessel regions are segmented in the portal phase. Second, rigid registration is performed between the liver regions to align the liver positions affected by the patient"s posture or breathing. Finally, fatty liver diagnosis is performed with the average Hounsfield unit (HU) value calculated using only the area removed from the vessel area segmented in the portal phase after registration in the precontrast liver area. The mean distance error between the points corresponding to the liver boundary was 3.136 mm and the mean error between the anatomic landmarks was 4.166 mm. A fatty liver diagnosis was confirmed in a total of 18 cases, and the results were identical to the histology results. This technique may be valuable in clinically diagnosing fatty liver using liver CT imaging, which is widely available and more commonly used than abdominal magnetic resonance.-
dc.description.statementOfResponsibilityrestriction-
dc.relation.isPartOfJournal of Computing Science and Engineering-
dc.rightsCC BY-NC-ND 2.0 KR-
dc.titleSegmentation and Rigid Registration of Liver Dynamic Computed Tomography Images for Diagnostic Assessment of Fatty Liver Disease-
dc.typeArticle-
dc.contributor.collegeCollege of Medicine (의과대학)-
dc.contributor.departmentDept. of Radiology (영상의학교실)-
dc.contributor.googleauthorKyoyeong Koo-
dc.contributor.googleauthorJeongjin Lee-
dc.contributor.googleauthorJiwon Hwang Hanwha Vision-
dc.contributor.googleauthorTaeyong Park-
dc.contributor.googleauthorHeeryeol Jeong-
dc.contributor.googleauthorSeungwoo Khang-
dc.contributor.googleauthorJongmyoung Lee-
dc.contributor.googleauthorHyuk Kwon-
dc.contributor.googleauthorSeungwon Na-
dc.contributor.googleauthorSunyoung Lee-
dc.contributor.googleauthorKyoung Won Kim-
dc.contributor.googleauthorKyung Won Kim-
dc.identifier.doi10.5626/jcse.2023.17.3.117-
dc.contributor.localIdA05659-
dc.identifier.urlhttps://www.dbpia.co.kr/journal/articleDetail?nodeId=NODE11557863-
dc.subject.keywordFatty liver-
dc.subject.keywordLiver CT imaging-
dc.subject.keywordSegmentation-
dc.subject.keywordRigid registration-
dc.subject.keywordDiagnosis-
dc.contributor.alternativeNameLee, Sunyoung-
dc.contributor.affiliatedAuthor이선영-
dc.citation.volume17-
dc.citation.number3-
dc.citation.startPage117-
dc.citation.endPage126-
dc.identifier.bibliographicCitationJournal of Computing Science and Engineering, Vol.17(3) : 117-126, 2023-09-
Appears in Collections:
1. College of Medicine (의과대학) > Dept. of Radiology (영상의학교실) > 1. Journal Papers

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