A novel Markov random field model based on region adjacency graph for T1 magnetic resonance imaging brain segmentation |
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Authors: | Ali Ahmadvand Sahar Yousefi M T Manzuri Shalmani |
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Affiliation: | 1. Mathematics and Computer Science Department, Emory University, Atlanta, GA;2. Department of Computer Engineering, Sharif University of Technology, Tehran, Iran |
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Abstract: | Tissue segmentation in magnetic resonance brain scans is the most critical task in different aspects of brain analysis. Because manual segmentation of brain magnetic resonance imaging (MRI) images is a time‐consuming and labor‐intensive procedure, automatic image segmentation is widely used for this purpose. As Markov Random Field (MRF) model provides a powerful tool for segmentation of images with a high level of artifacts, it has been considered as a superior method. But because of the high computational cost of MRF, it is not appropriate for online processing. This article has proposed a novel method based on a proper combination of MRF model and watershed algorithm in order to alleviate the MRF's drawbacks. Results illustrate that the proposed method has a good ability in MRI image segmentation, and also decreases the computational time effectively, which is a valuable improvement in the online applications. © 2017 Wiley Periodicals, Inc. Int J Imaging Syst Technol, 27, 78–88, 2017 |
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Keywords: | brain segmentation magnetic resonance imaging (MRI) Markov random field (MRF) watershed algorithm |
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