Please use this identifier to cite or link to this item: https://doi.org/10.1007/978-3-642-23626-6_52
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dc.titleOrientation histograms as shape priors for left ventricle segmentation using graph cuts
dc.contributor.authorMahapatra, D.
dc.contributor.authorSun, Y.
dc.date.accessioned2014-06-19T03:22:35Z
dc.date.available2014-06-19T03:22:35Z
dc.date.issued2011
dc.identifier.citationMahapatra, D.,Sun, Y. (2011). Orientation histograms as shape priors for left ventricle segmentation using graph cuts. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 6893 LNCS (PART 3) : 420-427. ScholarBank@NUS Repository. <a href="https://doi.org/10.1007/978-3-642-23626-6_52" target="_blank">https://doi.org/10.1007/978-3-642-23626-6_52</a>
dc.identifier.isbn9783642236259
dc.identifier.issn03029743
dc.identifier.urihttp://scholarbank.nus.edu.sg/handle/10635/71337
dc.description.abstractPoor contrast in magnetic resonance images makes cardiac left ventricle (LV) segmentation a very challenging task. We propose a novel graph cut framework using shape priors for segmentation of the LV from dynamic cardiac perfusion images. The shape prior information is obtained from a single image clearly showing the LV. The shape penalty is assigned based on the orientation angles between a pixel and all edge points of the prior shape. We observe that the orientation angles have distinctly different distributions for points inside and outside the LV. To account for shape change due to deformations, pixels near the boundary of the prior shape are allowed to change their labels by appropriate formulation of the penalty and smoothness terms. Experimental results on real patient datasets show our method's superior performance compared to two similar methods. © 2011 Springer-Verlag.
dc.description.urihttp://libproxy1.nus.edu.sg/login?url=http://dx.doi.org/10.1007/978-3-642-23626-6_52
dc.sourceScopus
dc.typeConference Paper
dc.contributor.departmentELECTRICAL & COMPUTER ENGINEERING
dc.description.doi10.1007/978-3-642-23626-6_52
dc.description.sourcetitleLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
dc.description.volume6893 LNCS
dc.description.issuePART 3
dc.description.page420-427
dc.identifier.isiutNOT_IN_WOS
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