A fuzzy clustering with bounded spatial probability for image segmentation

Accurate image segmentation is an important issue in image processing, where unsupervised clustering models play an important part and have been proven to be effective. However, most clustering methods suffer from limited segmentation accuracy without considering spatial information or bounded support region for practical data. In this paper, a bounded spatial probability based fuzzy clustering algorithm is proposed for image segmentation. A bounded distribution to fit the bounded data is utilized and a new conditional probability is constructed based on the immediate neighboring probabilities. Then a parameter-free mean template is presented to impose the spatial information more precisely. Finally, the negative logarithmical conditional probability is utilized as the dissimilarity function to describe the observed data. We evaluated our algorithm against several state-of-the-art segmentation approaches on brain magnetic resonance images. Our results suggest that the proposed algorithm is more robust to noise and textures, and can produce more accurate segmentation results.