title: Integrating spatial fuzzy clustering with level set methods for automated medical image segmentation.
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.
authors.affiliation: NUS Graduate School for Integrative Science and Engineering, Vision & Image Processing Lab, National University of Singapore, Singapore. bingoon@ieee.org
authors.forename: Bing Nan
authors.initials: BN
authors.lastname: Li
authors.forename: Chee Kong
authors.initials: CK
authors.lastname: Chui
authors.forename: Stephen
authors.initials: S
authors.lastname: Chang
authors.forename: S H
authors.initials: SH
authors.lastname: Ong
mesh_terms: D000465:Algorithms; D001921:Brain / Q000033:anatomy & histology; D002339:Carotid Arteries / Q000000981:diagnostic imaging; D016000:Cluster Analysis; D003952:Diagnostic Imaging / Q000379:methods*; D017143:Fuzzy Logic; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D008099:Liver / Q000000981:diagnostic imaging; D011859:Radiography; D014463:Ultrasonography