In this paper, we propose a new non-homogeneous Markov random field model based on fuzzy membership to resolve over-segmentation caused by traditional MRF model in the application of Brain MRI segmentation. Herein, we use fuzzy membership to estimate the parameters in the model. Simulated brain MRIs with the noise of different intensity and real brain MRIs are utilized in experiments. The results illustrate our method effectively reduces over-segmentation and improves final segmentation results and precision, and its performance is more powerful than that of kernel-based fuzzy c-means clustering algorithm and the traditional MRF model.

Date Published: 30 October 2009
PDF: 6 pages
Proc. SPIE 7497, MIPPR 2009: Medical Imaging, Parallel Processing of Images, and Optimization Techniques, 74972F (30 October 2009); doi: 10.1117/12.832160

Published in SPIE Proceedings Vol. 7497:
MIPPR 2009: Medical Imaging, Parallel Processing of Images, and Optimization Techniques
Faxiong Zhang; Faxiong Zhang, Editor(s)