Motivated by fuzzy clustering incorporating spatial information, we present a spatially constrained fuzzy hyper-prototype clustering algorithm in this paper. This approach uses hyperplanes as cluster centers and adds a spatial regularizer into the fuzzy objective function. Formulation of the new fuzzy objective function is presented; and its iterative numerical solution, which minimizes the objective function, derived. We applied the proposed algorithm for the segmentation of brain MRI data. Experimental results have demonstrated that the proposed clustering method outperforms other fuzzy clustering models.
['Jin Liu', 'Tuan D. Pham', 'Wei Wen', 'Perminder S. Sachdev']
Spatially constrained fuzzy hyper-prototype clustering with application to brain tissue segmentation