Please use this identifier to cite or link to this item: https://scholarbank.nus.edu.sg/handle/10635/39473
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dc.titleSet-based Cascading Approaches for Magnetic Resonance (MR) Image Segmentation (SCAMIS).
dc.contributor.authorLiu, J.
dc.contributor.authorLeong, T.Y.
dc.contributor.authorChee, K.B.
dc.contributor.authorTan, B.P.
dc.contributor.authorShuter, B.
dc.contributor.authorWang, S.C.
dc.date.accessioned2013-07-04T07:42:23Z
dc.date.available2013-07-04T07:42:23Z
dc.date.issued2006
dc.identifier.citationLiu, J.,Leong, T.Y.,Chee, K.B.,Tan, B.P.,Shuter, B.,Wang, S.C. (2006). Set-based Cascading Approaches for Magnetic Resonance (MR) Image Segmentation (SCAMIS).. AMIA ... Annual Symposium proceedings / AMIA Symposium. AMIA Symposium : 504-508. ScholarBank@NUS Repository.
dc.identifier.issn15594076
dc.identifier.urihttp://scholarbank.nus.edu.sg/handle/10635/39473
dc.description.abstractThis paper introduces Set-based Cascading Approach for Medical Image Segmentation (SCAMIS), a new methodology for segmentation of medical imaging by integrating a number of algorithms. Existing approaches typically adopt the pipeline methodology. Although these methods provide promising results, the results generated are still susceptible to over-segmentation and leaking. In our methodology, we describe how set operations can be utilized to better overcome these problems. To evaluate the effectiveness of this approach, Magnetic Resonance Images taken from a teaching hospital research programme have been utilised, to reflect the real world quality needed for testing in patient datasets. A comparison between the pipeline and set-based methodology is also presented.
dc.sourceScopus
dc.typeArticle
dc.contributor.departmentCOMPUTER SCIENCE
dc.description.sourcetitleAMIA ... Annual Symposium proceedings / AMIA Symposium. AMIA Symposium
dc.description.page504-508
dc.identifier.isiutNOT_IN_WOS
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