Please use this identifier to cite or link to this item: https://doi.org/10.1109/TBME.2010.2093523
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dc.titleSegmentation of liver vasculature from contrast enhanced CT images using context-based voting
dc.contributor.authorChi, Y.
dc.contributor.authorLiu, J.
dc.contributor.authorVenkatesh, S.K.
dc.contributor.authorHuang, S.
dc.contributor.authorZhou, J.
dc.contributor.authorTian, Q.
dc.contributor.authorNowinski, W.L.
dc.date.accessioned2016-09-06T09:10:18Z
dc.date.available2016-09-06T09:10:18Z
dc.date.issued2011-08
dc.identifier.citationChi, Y., Liu, J., Venkatesh, S.K., Huang, S., Zhou, J., Tian, Q., Nowinski, W.L. (2011-08). Segmentation of liver vasculature from contrast enhanced CT images using context-based voting. IEEE Transactions on Biomedical Engineering 58 (8) : 2144-2153. ScholarBank@NUS Repository. https://doi.org/10.1109/TBME.2010.2093523
dc.identifier.issn00189294
dc.identifier.urihttp://scholarbank.nus.edu.sg/handle/10635/126956
dc.description.abstractA novel vessel context-based voting is proposed for automatic liver vasculature segmentation in CT images. It is able to conduct full vessel segmentation and recognition of multiple vasculatures effectively. The vessel context describes context information of a voxel related to vessel properties, such as intensity, saliency, direction, and connectivity. Voxels are grouped to liver vasculatures hierarchically based on vessel context. They are first grouped locally into vessel branches with the advantage of a vessel junction measurement and then grouped globally into vasculatures, which is implemented using a multiple feature point voting mechanism. The proposed method has been evaluated on ten clinical CT datasets. Segmentation of third-order vessel trees from CT images (0.76×0.76 2.0 mm) of the portal venous phase takes less than 3 min on a PC with 2.0 GHz dual core processor and the average segmentation accuracy is up to 98. © 2011 IEEE.
dc.description.urihttp://libproxy1.nus.edu.sg/login?url=http://dx.doi.org/10.1109/TBME.2010.2093523
dc.sourceScopus
dc.subjectLiver vasculature segmentation
dc.subjectmultiple feature point voting
dc.subjectvessel context
dc.subjectvessel junction measure
dc.typeArticle
dc.contributor.departmentDIAGNOSTIC RADIOLOGY
dc.description.doi10.1109/TBME.2010.2093523
dc.description.sourcetitleIEEE Transactions on Biomedical Engineering
dc.description.volume58
dc.description.issue8
dc.description.page2144-2153
dc.description.codenIEBEA
dc.identifier.isiut000293700800002
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