title: A mean shift based fuzzy c-means algorithm for image segmentation.
abstract: Image segmentation is an important task in many medical applications. One family of segmentation algorithms is based on the idea of clustering pixels with similar characteristics. C-means based approaches, in particular fuzzy c-means has been shown to work well for clustering based segmentation, however due to the iterative nature are also computationally complex. In this paper we introduce a new mean shift based fuzzy c-means algorithm that we show to be faster than previous techniques while providing good segmentation performance. The proposed clustering method incorporates a mean field term within the standard fuzzy c-means objective function. Since mean shift can quickly and reliably find cluster centres, the entire strategy is capable of optimally segmenting clusters within an image.
authors.affiliation: School of Engineering and Design, Brunel University, U.K. Huiyu.Zhou@brunel.ac.uk
authors.forename: Huiyu
authors.initials: H
authors.lastname: Zhou
authors.forename: Gerald
authors.initials: G
authors.lastname: Schaefer
authors.forename: Chunmei
authors.initials: C
authors.lastname: Shi
mesh_terms: D000465:Algorithms; D016880:Anisotropy; D001921:Brain / Q000473:pathology*; D016000:Cluster Analysis; D003952:Diagnostic Imaging / Q000379:methods*; D017143:Fuzzy Logic; D006801:Humans; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D007091:Image Processing, Computer-Assisted; D015233:Models, Statistical; D008962:Models, Theoretical; D010363:Pattern Recognition, Automated / Q000379:methods; D015203:Reproducibility of Results; D012984:Software