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Tooth Segmentation Using Gaussian Mixture Model and Genetic Algorithm

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dc.contributor.author김기덕-
dc.contributor.author김주영-
dc.contributor.author박원서-
dc.contributor.author유선국-
dc.contributor.author장원석-
dc.date.accessioned2018-07-20T08:34:45Z-
dc.date.available2018-07-20T08:34:45Z-
dc.date.issued2017-
dc.identifier.issn2156-7018-
dc.identifier.urihttps://ir.ymlib.yonsei.ac.kr/handle/22282913/161299-
dc.description.abstractBackground: The present study suggested an image segmentation method for dental cone beam computed tomography (CBCT) data with a proposed preprocessing step and genetic algorithm. Segmentation of dental CT images is often hampered by the proximity of teeth and alveolar bones that display similar brightness. The present study sought to overcome this difficulty by using a Gaussian mixture model (GMM) and contrast-limited adaptive histogram equalization (CLAHE) in the preprocessing step. First, the original dental image was processed by GMM to eliminate regions other than the teeth and alveolar bones. Then, we composed the preprocessed image by enhancing tooth contours through application of CLAHE. Finally, tooth and pulp regions were extracted via the evolutionary process of genetic algorithm. We confirmed that tooth segmentation using a genetic algorithm was effective in segmenting teeth that are adjacent and have similar shapes and brightness.-
dc.description.statementOfResponsibilityrestriction-
dc.languageUnited States-
dc.publisher2156-7026-
dc.relation.isPartOfJOURNAL OF MEDICAL IMAGING AND HEALTH INFORMATICS-
dc.rightsCC BY-NC-ND 2.0 KR-
dc.rightshttps://creativecommons.org/licenses/by-nc-nd/2.0/kr/-
dc.titleTooth Segmentation Using Gaussian Mixture Model and Genetic Algorithm-
dc.typeArticle-
dc.contributor.collegeCollege of Dentistry-
dc.contributor.departmentDept. of Advanced General Dentistry-
dc.contributor.googleauthorKim, Joo Young-
dc.contributor.googleauthorYoo, Sun K.-
dc.contributor.googleauthorJang, W. S.-
dc.contributor.googleauthorPark, Byung Eun-
dc.contributor.googleauthorPark, Wonse-
dc.contributor.googleauthorKim, Kee Deog-
dc.identifier.doi10.1166/jmihi.2017.2251-
dc.contributor.localIdA00332-
dc.contributor.localIdA00939-
dc.contributor.localIdA01589-
dc.contributor.localIdA02471-
dc.contributor.localIdA04793-
dc.relation.journalcodeJ03359-
dc.identifier.eissn2156-7026-
dc.identifier.urlhttps://www.ingentaconnect.com/contentone/asp/jmihi/2017/00000007/00000006/art00022-
dc.subject.keywordBIOMEDICAL ENGINEERING-
dc.subject.keywordCONE BEAM CT-
dc.subject.keywordCONTRAST-LIMITED ADAPTIVE HISTOGRAM EQUALIZATION-
dc.subject.keywordGAUSSIAN MIXTURE MODEL-
dc.subject.keywordGENETIC ALGORITHM-
dc.subject.keywordTOOTH SEGMENTATION-
dc.contributor.alternativeNameKim, Kee Deog-
dc.contributor.alternativeNameKim, Joo Young-
dc.contributor.alternativeNamePark, Wonse-
dc.contributor.alternativeNameYoo, Sun Kook-
dc.contributor.alternativeNameChang, Won Seok-
dc.contributor.affiliatedAuthorKim, Kee Deog-
dc.contributor.affiliatedAuthorKim, Joo Young-
dc.contributor.affiliatedAuthorPark, Wonse-
dc.contributor.affiliatedAuthorYoo, Sun Kook-
dc.contributor.affiliatedAuthorChang, Won Seok-
dc.citation.volume7-
dc.citation.number6-
dc.citation.startPage1271-
dc.citation.endPage1276-
dc.identifier.bibliographicCitationJOURNAL OF MEDICAL IMAGING AND HEALTH INFORMATICS, Vol.7(6) : 1271-1276, 2017-
Appears in Collections:
2. College of Dentistry (치과대학) > Dept. of Advanced General Dentistry (통합치의학과) > 1. Journal Papers
1. College of Medicine (의과대학) > Dept. of Medical Engineering (의학공학교실) > 1. Journal Papers

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