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dc.contributor.authorLi, Changyang
dc.contributor.authorLi, Ang
dc.contributor.authorWang, Xiuying
dc.contributor.authorFeng, Dagan
dc.contributor.authorEberl, Stefan
dc.contributor.authorFulham, Michael
dc.date.accessioned2020-02-10
dc.date.available2020-02-10
dc.date.issued2015-03-23
dc.identifier.citationC. Li, A. Li, X. Wang, D. Feng, S. Eberl and M. Fulham, "A new statistical and Dirichlet integral framework applied to liver segmentation from volumetric CT images," 2014 13th International Conference on Control Automation Robotics & Vision (ICARCV), Singapore, 2014, pp. 642-647. doi: 10.1109/ICARCV.2014.7064379en_AU
dc.identifier.urihttps://hdl.handle.net/2123/21809
dc.description.abstractAccurate liver segmentation from computed tomography (CT) images is problematic due to non-uniform density, weak boundaries and because there may be multiple liver tumors that have heterogeneous intensities in region(s) of interest (ROIs). So we propose a generalized energy framework that harnesses the statistical intensity approximation with image data on graphs. Our statistical energy term takes advantage of the mixture-of-mixtures Gaussian model to approximate the probability density distribution of the liver and background to better differentiate between the two. The probability density estimation can be combined with the spatial cohesion of the graph-based Dirichlet integral by using graph calculus. Matrix decomposition and differentiation are used to minimize our proposed energy functional. We tested our approach on 20 public high-contrast CT images with single and multiple liver tumors. Our method had an average dice similarity coefficient (DSC) of 93.75±1.29%, an average false positive (FP) rate of 9.43±3.52% and an average false negative (FN) rate of 3.48±1.48%. Our method outperformed the benchmark graph-based Random Walker algorithm (average DSC=81.97±4.09%, average FP rate 34.10±10.53%, and average FN rate 7.10±4.35%).en_AU
dc.description.sponsorshipARCen_AU
dc.language.isoen_AUen_AU
dc.publisherIEEEen_AU
dc.relationARC LP140100686en_AU
dc.rights“© 2014 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.”en_AU
dc.subjectImage segmentation, mixture-of-mixtures Gaussian, Dirichlet integral, energy miminizationen_AU
dc.titleA new statistical and Dirichlet integral framework applied to liver segmentation from volumetric CT imagesen_AU
dc.typeConference paperen_AU
dc.subject.asrc080106 - Image Processingen_AU
dc.subject.asrc080109 - Pattern Recognition and Data Miningen_AU
dc.identifier.doi10.1109/ICARCV.2014.7064379
dc.type.pubtypePost-printen_AU


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