We present a stochastic model based technique that uses the concept of deterministic annealing to obtain a generalized solution to the nonconvex optimization problem encountered by many image segmentation techniques. Deterministic annealing [DA] is an elegant and useful tool for clustering and classification. This novel optimization approach works with the efficiency of a deterministic procedure and has been successfully applied to a number of combinatorial optimization problems. In this paper, we demonstrate effective segmentation of simulated MR brain images and provide a quality measure for accuracy of classification. A generalized deterministic annealing procedure, which works under a structural constraint of mass or density, has been utilized for this purpose. This method produces a hierarchy of solutions giving segmentation results from a coarse to a fine level. Automatic edge detection can be performed using these solutions that are at different degrees of coarseness. The procedure has been made more efficient by utilizing a new similarity parameter from the concepts of neuro-fuzzy clustering.
|Number of pages||3|
|Journal||Annual Conference of the North American Fuzzy Information Processing Society - NAFIPS|
|State||Published - 2000|
|Event||19th International Confernce of the North American Fuzzy Information Processing Society-NAFIPS (PEACH FUZZ 2000) - Atlanta, GA, USA|
Duration: Jul 13 2000 → Jul 15 2000