Integrating spatial fuzzy clustering with level set methods for automated medical image segmentation

Authors:
Bing Nan Li;Chee Kong Chui;Stephen Chang;S. H. Ong
Affiliations:
NUS Graduate School for Integrative Science and Engineering, National University of Singapore, Singapore and Division of Bioengineering, National University of Singapore, Singapore;Department of Mechanical Engineering, National University of Singapore, Singapore;Department of Surgery, National University Hospital, Kent Ridge Wing 2, Singapore;Department of Electrical and Computer Engineering, National University of Singapore, Singapore and Division of Bioengineering, National University of Singapore, Singapore
Venue:
Computers in Biology and Medicine
Year:
2011

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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.