Please use this identifier to cite or link to this item: https://doi.org/10.1007/11566465_34
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dc.titleSegmentation of neighboring organs in medical image with model competition
dc.contributor.authorYan, P.
dc.contributor.authorShen, W.
dc.contributor.authorKassim, A.A.
dc.contributor.authorShah, M.
dc.date.accessioned2014-06-19T03:27:06Z
dc.date.available2014-06-19T03:27:06Z
dc.date.issued2005
dc.identifier.citationYan, P.,Shen, W.,Kassim, A.A.,Shah, M. (2005). Segmentation of neighboring organs in medical image with model competition. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 3749 LNCS : 270-277. ScholarBank@NUS Repository. <a href="https://doi.org/10.1007/11566465_34" target="_blank">https://doi.org/10.1007/11566465_34</a>
dc.identifier.isbn3540293272
dc.identifier.issn03029743
dc.identifier.urihttp://scholarbank.nus.edu.sg/handle/10635/71727
dc.description.abstractThis paper presents a novel approach for image segmentation by introducing competition between neighboring shape models. Our method is motivated by the observation that evolving neighboring contours should avoid overlapping with each other and this should be able to aid in multiple neighboring objects segmentation. A novel energy functional is proposed, which incorporates both prior shape information and interactions between deformable models. Accordingly, we also propose an extended maximum a posteriori (MAP) shape estimation model to obtain the shape estimate of the organ. The contours evolve under the influence of image information, their own shape priors and neighboring MAP shape estimations using level set methods to recover organ shapes. Promising results and comparisons from experiments on both synthetic data and medical imagery demonstrate the potential of our approach. © Springer-Verlag Berlin Heidelberg 2005.
dc.description.urihttp://libproxy1.nus.edu.sg/login?url=http://dx.doi.org/10.1007/11566465_34
dc.sourceScopus
dc.typeConference Paper
dc.contributor.departmentELECTRICAL & COMPUTER ENGINEERING
dc.description.doi10.1007/11566465_34
dc.description.sourcetitleLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
dc.description.volume3749 LNCS
dc.description.page270-277
dc.identifier.isiutNOT_IN_WOS
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