A new nonhomogeneous Markov random field model based on fuzzy membership for brain MRI segmentation

Rong Xu; Limin Luo

doi:10.1117/12.832160

30 October 2009 A new nonhomogeneous Markov random field model based on fuzzy membership for brain MRI segmentation

Rong Xu, Limin Luo

Proceedings Volume 7497, MIPPR 2009: Medical Imaging, Parallel Processing of Images, and Optimization Techniques; 74972F (2009) https://doi.org/10.1117/12.832160
Event: Sixth International Symposium on Multispectral Image Processing and Pattern Recognition, 2009, Yichang, China

Abstract

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.

Citation Download Citation

Rong Xu and Limin Luo "A new nonhomogeneous Markov random field model based on fuzzy membership for brain MRI segmentation", Proc. SPIE 7497, MIPPR 2009: Medical Imaging, Parallel Processing of Images, and Optimization Techniques, 74972F (30 October 2009); https://doi.org/10.1117/12.832160

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