Loading...

A novel Markov random field model based on region adjacency graph for T1 magnetic resonance imaging brain segmentation

Ahmadvand, A ; Sharif University of Technology | 2017

435 Viewed
  1. Type of Document: Article
  2. DOI: 10.1002/ima.22212
  3. Publisher: John Wiley and Sons Inc , 2017
  4. Abstract:
  5. 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. © 2017 Wiley Periodicals, Inc
  6. Keywords:
  7. Magnetic resonance imaging (MRI) ; Markov random field (MRF) ; Watershed algorithm ; Magnetic resonance imaging ; Magnetism ; Markov processes ; Resonance ; Watersheds ; Automatic image segmentation ; Brain segmentation ; Markov Random Field model ; Markov random field models ; Markov Random Fields ; On-line applications ; Region adjacency graphs ; Water-shed algorithm ; Image segmentation
  8. Source: International Journal of Imaging Systems and Technology ; Volume 27, Issue 1 , 2017 , Pages 78-88 ; 08999457 (ISSN)
  9. URL: https://onlinelibrary.wiley.com/doi/full/10.1002/ima.22212