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https://scholarbank.nus.edu.sg/handle/10635/25583
DC Field | Value | |
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dc.title | A Set-based Hybrid Approach (SHA) for MRI segmentation | |
dc.contributor.author | Liu, J. | |
dc.contributor.author | Leong, T.-Y. | |
dc.contributor.author | Chee, K.B. | |
dc.contributor.author | Tan, B.P. | |
dc.contributor.author | Shuter, B. | |
dc.contributor.author | Wang, S.-C. | |
dc.date.accessioned | 2011-08-16T05:50:53Z | |
dc.date.available | 2011-08-16T05:50:53Z | |
dc.date.issued | 2006 | |
dc.identifier.citation | Liu, J., Leong, T.-Y., Chee, K.B., Tan, B.P., Shuter, B., Wang, S.-C. (2006). A Set-based Hybrid Approach (SHA) for MRI segmentation. 9th International Conference on Control, Automation, Robotics and Vision, 2006, ICARCV '06. ScholarBank@NUS Repository. | |
dc.identifier.isbn | 1424403421 | |
dc.identifier.isbn | 9781424403424 | |
dc.identifier.uri | http://scholarbank.nus.edu.sg/handle/10635/25583 | |
dc.description.abstract | This paper describes a new hybrid approach Set-Based Hybrid Approach (SHA) for Magnetic Resonance (MR) image segmentation by integrating two existing techniques, region-grow and threshold level set. To evaluate the proposed approach in performing real world image segmentation task, instead of using well-taken MR-images, we use real-life images collected in a hospital. Comparison of the performance between the two individual techniques and the new hybrid technique demonstrates the effectiveness of the latter. © 2006 IEEE. | |
dc.description.uri | http://libproxy1.nus.edu.sg/login?url=http://dx.doi.org/10.1109/ICARCV.2006.345358 | |
dc.source | Scopus | |
dc.type | Conference Paper | |
dc.contributor.department | COMPUTER SCIENCE | |
dc.contributor.department | DIAGNOSTIC RADIOLOGY | |
dc.description.sourcetitle | 9th International Conference on Control, Automation, Robotics and Vision, 2006, ICARCV '06 | |
dc.identifier.isiut | NOT_IN_WOS | |
Appears in Collections: | Staff Publications |
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