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dc.contributor.author김명희*
dc.date.accessioned2016-08-28T11:08:32Z-
dc.date.available2016-08-28T11:08:32Z-
dc.date.issued2000*
dc.identifier.issn0097-8493*
dc.identifier.otherOAK-557*
dc.identifier.urihttps://dspace.ewha.ac.kr/handle/2015.oak/218713-
dc.description.abstractDue to the development of new imaging devices, which produce a large number of tomographic slices, advanced techniques for the evaluation of large amounts of data are required. Therefore, computer-supported extraction of dynamic 3-D models of patient anatomy from temporal series is highly desirable. Since the diagnostician must be able to quickly make rational decisions based on the models, a high degree of accuracy is required within a minimum amount of time. We present modelling and visualization techniques that are realized within the Cardiac Station. Results for the application of these techniques to cardiac image data are given. In addition to providing information about the patient's morphology, functional parameters can be derived from the data and visualized together with the model. In order to verify the model with the original image data and to plan for real intervention, interaction techniques are presented.*
dc.languageEnglish*
dc.publisherElsevier Science Ltd, Exeter, United Kingdom*
dc.titleModelling, visualization, and interaction techniques for diagnosis and treatment planning in cardiology*
dc.typeArticle*
dc.relation.issue5*
dc.relation.volume24*
dc.relation.indexSCIE*
dc.relation.indexSCOPUS*
dc.relation.startpage741*
dc.relation.lastpage753*
dc.relation.journaltitleComputers and Graphics (Pergamon)*
dc.identifier.doi10.1016/S0097-8493(00)00076-5*
dc.identifier.wosidWOS:000165255200009*
dc.identifier.scopusid2-s2.0-0034291675*
dc.author.googleBehr J.*
dc.author.googleChoi S.-M.*
dc.author.googleGrosskopf S.*
dc.author.googleHong H.*
dc.author.googleNam S.-A.*
dc.author.googlePeng Y.*
dc.author.googleHildebrand A.*
dc.author.googleKim M.-H.*
dc.author.googleSakas G.*
dc.contributor.scopusid김명희(34770838100)*
dc.date.modifydate20240322133114*
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인공지능대학 > 컴퓨터공학과 > Journal papers
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