A novel Markov random field model based on region adjacency graph for T1 magnetic resonance imaging brain segmentation. Issue 1 (21st March 2017)

• Record Type: Journal Article
• Authors: Ahmadvand, Ali; Yousefi, Sahar; Manzuri Shalmani, M. T.
• Is Part Of: International journal of imaging systems and technology. Volume 27:Issue 1(2017:Mar.)
• Page Start: 78
• Page End: 88
• Publication Date: 2017-03-21
• Subjects: brain segmentation -- magnetic resonance imaging (MRI) -- Markov random field (MRF) -- watershed algorithm; Imaging systems -- Periodicals; Image processing -- Periodicals; 621.367
• DOI: 10.1002/ima.22212
• Languages: English
• ISSNs: 0899-9457

Abstract

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