Please use this identifier to cite or link to this item: https://doi.org/10.1109/IEMBS.2007.4352411
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dc.titleMedical image segmentation using feature-based GVF snake
dc.contributor.authorNg, H.P.
dc.contributor.authorFoong, K.W.C.
dc.contributor.authorOng, S.H.
dc.contributor.authorGoh, P.S.
dc.contributor.authorNowinski, W.L.
dc.date.accessioned2014-06-19T03:17:40Z
dc.date.available2014-06-19T03:17:40Z
dc.date.issued2007
dc.identifier.citationNg, H.P., Foong, K.W.C., Ong, S.H., Goh, P.S., Nowinski, W.L. (2007). Medical image segmentation using feature-based GVF snake. Annual International Conference of the IEEE Engineering in Medicine and Biology - Proceedings : 800-803. ScholarBank@NUS Repository. https://doi.org/10.1109/IEMBS.2007.4352411
dc.identifier.isbn1424407885
dc.identifier.issn05891019
dc.identifier.urihttp://scholarbank.nus.edu.sg/handle/10635/70909
dc.description.abstractWe propose a feature-based GVF snake for medical image segmentation here. Feature-based criteria are introduced for the GVF snake to stop its iterations. Without these criteria, the GVF snake might continue its iterations even though it has converged at the targeted object and result in longer computational time. The feature here is the area of the targeted object. Our proposed method comprises of two stages, namely the training stage and the segmentation stage. In the training stage, we acquire prior knowledge on the relative area of the targeted object from training data. In the segmentation stage, the proposed feature-based GVF snake is applied to segment the object from the image after computing the estimated area of the targeted object. In our proposed method, the GVF snake stops its iterations when the area bounded by its propagation is approximately equal to the estimated area and when it undergoes little change over two consecutive iterations. To illustrate the effectiveness of our proposed method, we applied it to the segmentation of the masseter muscle, which is the strongest jaw muscle, from 2-D magnetic resonance (MR) images. Numerical evaluation done indicates good agreement between the computerized and manual segmentations, with mean overlap of 92%. © 2007 IEEE.
dc.description.urihttp://libproxy1.nus.edu.sg/login?url=http://dx.doi.org/10.1109/IEMBS.2007.4352411
dc.sourceScopus
dc.typeConference Paper
dc.contributor.departmentPREVENTIVE DENTISTRY
dc.contributor.departmentELECTRICAL & COMPUTER ENGINEERING
dc.description.doi10.1109/IEMBS.2007.4352411
dc.description.sourcetitleAnnual International Conference of the IEEE Engineering in Medicine and Biology - Proceedings
dc.description.page800-803
dc.description.codenCEMBA
dc.identifier.isiut000253467000203
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