Please use this identifier to cite or link to this item: https://doi.org/10.1109/EMBC.2012.6346783
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dc.titleA semi-automatic approach to the segmentation of liver parenchyma from 3D CT images with extreme learning machine
dc.contributor.authorHuang, W.
dc.contributor.authorTan, Z.M.
dc.contributor.authorLin, Z.
dc.contributor.authorHuang, G.-B.
dc.contributor.authorZhou, J.
dc.contributor.authorChui, C.K.
dc.contributor.authorSu, Y.
dc.contributor.authorChang, S.
dc.date.accessioned2014-06-19T05:31:03Z
dc.date.available2014-06-19T05:31:03Z
dc.date.issued2012
dc.identifier.citationHuang, W.,Tan, Z.M.,Lin, Z.,Huang, G.-B.,Zhou, J.,Chui, C.K.,Su, Y.,Chang, S. (2012). A semi-automatic approach to the segmentation of liver parenchyma from 3D CT images with extreme learning machine. Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS : 3752-3755. ScholarBank@NUS Repository. <a href="https://doi.org/10.1109/EMBC.2012.6346783" target="_blank">https://doi.org/10.1109/EMBC.2012.6346783</a>
dc.identifier.isbn9781424441198
dc.identifier.issn1557170X
dc.identifier.urihttp://scholarbank.nus.edu.sg/handle/10635/73091
dc.description.abstractThis paper presents a semi-automatic approach to segmentation of liver parenchyma from 3D computed tomography (CT) images. Specifically, liver segmentation is formalized as a pattern recognition problem, where a given voxel is to be assigned a correct label - either in a liver or a non-liver class. Each voxel is associated with a feature vector that describes image textures. Based on the generated features, an Extreme Learning Machine (ELM) classifier is employed to perform the voxel classification. Since preliminary voxel segmentation tends to be less accurate at the boundary, and there are other non-liver tissue voxels with similar texture characteristics as liver parenchyma, morphological smoothing and 3D level set refinement are applied to enhance the accuracy of segmentation. Our approach is validated on a set of CT data. The experiment shows that the proposed approach with ELM has the reasonably good performance for liver parenchyma segmentation. It demonstrates a comparable result in accuracy of classification but with a much faster training and classification speed compared with support vector machine (SVM). © 2012 IEEE.
dc.description.urihttp://libproxy1.nus.edu.sg/login?url=http://dx.doi.org/10.1109/EMBC.2012.6346783
dc.sourceScopus
dc.typeConference Paper
dc.contributor.departmentMECHANICAL ENGINEERING
dc.description.doi10.1109/EMBC.2012.6346783
dc.description.sourcetitleProceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS
dc.description.page3752-3755
dc.identifier.isiutNOT_IN_WOS
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