|
DSpace@nitr >
National Institue of Technology- Rourkela >
Conference Papers >
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 |
| Appears in Collections: | Conference Papers
|
Files in This Item:
| File |
Description |
Size | Format |
|---|
| paper_aiconf_pune.pdf | | 837Kb | Adobe PDF | View/Open |
|
Show full item record
All items in DSpace are protected by copyright, with all rights reserved.
|
