Research on algorithms based on Markov random models for diffusion tensor-magnetic resonance images.
- Author:
Jie PENG
1
;
Qing-wen LÜ
;
Yan-qiu FENG
;
Yuan-yuan GAO
;
Wu-fan CHEN
Author Information
1. School of Biomedical Engineering, Southern Medical University. Guangzhou 510515, China.E-mail: cgirl1981@126.com.
- Publication Type:Journal Article
- MeSH:
Algorithms;
Diffusion Magnetic Resonance Imaging;
methods;
Humans;
Image Interpretation, Computer-Assisted;
methods;
Pattern Recognition, Automated
- From:
Journal of Southern Medical University
2010;30(7):1562-1572
- CountryChina
- Language:Chinese
-
Abstract:
With the utilization of diffusion tensor information of image voxels, a novel MRF (Markov Random Field) segmentation algorithm was proposed for diffusion tensor MRI (DT-MRI) images benefitted from the introduction of Frobenius norm. The comparison of the segmentation effects between the proposed algorithm and K-means segmentation algorithm for DT-MRI image was made, which showed that the new algorithm could segment the DT-MRI images more accurately than the K-means algorithm. Moreover, with the same segmentation algorithm of MRF, better outcomes were achieved in DT-MRI than in conventional MRI (T2WI) image.
