Adaptive spatially constrained fuzzy clustering for image segmentation

An adaptive, spatially constrained fuzzy clustering algorithm for image segmentation is presented. By using a novel dissimilarity index in the cost function, our fuzzy clustering algorithm is capable of utilising local contextual information in a 30 neighborhood to impose local spatial continuity, thus exploiting the high inter-pixel correlation inherent in most realworld images. This has the effects of smoothing out random noise and resolving classification ambiguities. By introducing a multiplicative bias field to the fixed cluster prototypes, the cluster prototypes effectively become adaptive to nonstationarity in the image intensity. This allows non-planar regions or objects to be segmented meaningfully. Experimental results have shown the effectiveness of the proposed fuzzy clustering algorithm.