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dc.contributor.authorKeil, Matthiasen_US
dc.contributor.authorLaura, Cristina Oyarzunen_US
dc.contributor.authorDrechsler, Klausen_US
dc.contributor.authorWesarg, Stefanen_US
dc.contributor.editorTimo Ropinski and Anders Ynnerman and Charl Botha and Jos Roerdinken_US
dc.date.accessioned2013-11-08T10:34:16Z
dc.date.available2013-11-08T10:34:16Z
dc.date.issued2012en_US
dc.identifier.isbn978-3-905674-38-5en_US
dc.identifier.issn2070-5778en_US
dc.identifier.urihttp://dx.doi.org/10.2312/VCBM/VCBM12/057-064en_US
dc.description.abstractMultimodal registration of intraoperative ultrasound and preoperative computed tomography imaging is the basis for percutaneous hepatic interventions. Currently, a rigid registration is performed manually by the surgeon using vessel structures and other anatomical landmarks for visual guidance. In this work our approach for intraoperative vessel segmentation from two ultrasound imaging modes, namely B-Mode and color flow mode, is presented. This segmentation is an important step for automation of the intraoperative registration which relies on vessel structures visible in contrast enhanced CT and ultrasound volumes. This paper describes the problems that arise when using B-mode ultrasound for segmentation of vessels and how they can be solved by introducing additional vessel information from color flow imaging. On a total number of 21 patients, our system was applied successfully in 15 cases. For nine randomly chosen patients studied in this paper, our system achieves a 3.45 mm accuracy at points used for registration and 5.01 mm for other landmarks which were not used for the registration process.en_US
dc.publisherThe Eurographics Associationen_US
dc.subjectImage Processing and Computer Vision [I.4.6]en_US
dc.subjectSegmentationen_US
dc.subjectImage Processing and Computer Vision [I.4.m]en_US
dc.subjectMiscellaneousen_US
dc.titleCombining B-Mode and Color Flow Vessel Segmentation for Registration of Hepatic CT and Ultrasound Volumesen_US
dc.description.seriesinformationEurographics Workshop on Visual Computing for Biology and Medicineen_US


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