Please use this identifier to cite or link to this item: https://doi.org/10.1016/j.compbiomed.2010.10.007
Title: Integrating spatial fuzzy clustering with level set methods for automated medical image segmentation
Authors: Li, B.N.
Chui, C.K. 
Chang, S.
Ong, S.H. 
Keywords: Adaptive clustering
Level set methods
Medical image segmentation
Spatial fuzzy clustering
Issue Date: Jan-2011
Citation: Li, B.N., Chui, C.K., Chang, S., Ong, S.H. (2011-01). Integrating spatial fuzzy clustering with level set methods for automated medical image segmentation. Computers in Biology and Medicine 41 (1) : 1-10. ScholarBank@NUS Repository. https://doi.org/10.1016/j.compbiomed.2010.10.007
Abstract: The performance of the level set segmentation is subject to appropriate initialization and optimal configuration of controlling parameters, which require substantial manual intervention. A new fuzzy level set algorithm is proposed in this paper to facilitate medical image segmentation. It is able to directly evolve from the initial segmentation by spatial fuzzy clustering. The controlling parameters of level set evolution are also estimated from the results of fuzzy clustering. Moreover the fuzzy level set algorithm is enhanced with locally regularized evolution. Such improvements facilitate level set manipulation and lead to more robust segmentation. Performance evaluation of the proposed algorithm was carried on medical images from different modalities. The results confirm its effectiveness for medical image segmentation. © 2010 Elsevier Ltd.
Source Title: Computers in Biology and Medicine
URI: http://scholarbank.nus.edu.sg/handle/10635/50953
ISSN: 00104825
DOI: 10.1016/j.compbiomed.2010.10.007
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