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|Title:||A Hybrid Approach using Set Theory (HAST) for Magnetic Resonance (MR) Image segmentation|
|Authors:||Jiang, L. |
|Citation:||Jiang, L.,Ban, C.K.,Pin, T.B.,Borys, S.,Wang, S.-C. (2006). A Hybrid Approach using Set Theory (HAST) for Magnetic Resonance (MR) Image segmentation. Proceedings of the 6th International Workshop on Pattern Recognition in Information Systems, PRIS 2006, in Conjunction with ICEIS 2006 : 159-168. ScholarBank@NUS Repository.|
|Abstract:||This paper describes a new Hybrid Approach using Set Theory (HAST) for Magnetic Resonance (MR) Image segmentation based on two existing techniques, region-based and level set methods. In our approach, instead of using the typical pipeline methodology to integrate the two techniques, a hybrid set-based methodology will be proposed. To evaluate the effectiveness of HAST, MR images taken from a national hospital that reflects the quality of real world medical images are used. A comparison between the two individual techniques and HAST will also be made to demonstrate the effectiveness of the latter.|
|Source Title:||Proceedings of the 6th International Workshop on Pattern Recognition in Information Systems, PRIS 2006, in Conjunction with ICEIS 2006|
|Appears in Collections:||Staff Publications|
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