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Please use this identifier to cite or link to this item: http://hdl.handle.net/2080/358

Title: Brain MR Image Segmentation Using Tabu Search and Hidden Markov Random Field Model
Authors: Nanda, P K
Patra, D
Pradhan, A
Issue Date: 2005
Citation: Proceedings of the 2nd Indian International Conference on Artificial Intelligence (IICAI 2005),
Abstract: In this paper, we propose a hybrid Tabu Expectation Maximization (TEM) Algorithm for segmentation of Brain Magnetic Resonance (MR) images in both supervised and unsupervised framewrok. Gaussian Hidden Markov Random Field (GHMRF) is used to model the available degraded image. In supervised framework, the apriori image MRF model parameters as well as the GHMRF model parameters are assumed to be known. The class labels are estimated using the Maximum a Posteriori (MAP) estimation criterion. In unsupervised framework, the problem of model parameter estimation and label estimation is formulated in Expectation Maximization (EM) framework. The labels are estimated using the proposed Tabu Search algorithm while the model parameters are the maximum likelihood estimates. Our proposed algorithm yields results with arbitrary initial paramater set and thus overcomes the problem of proper choice of initial parameters. The results obtained are comparable with the results obtained by usi...
Description: Copyright for this article belongs to proceedings publisher
URI: http://hdl.handle.net/2080/358
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