Brain MR image segmentation algorithm based on Markov random field with image patch

Without considering the spatial information between pixels, the traditional Gaussian mixture model (GMM) algorithm is very sensitive to noise during image segmentation. Markov random field (MRF) models provide a powerful way to noisy images through Gibbs joint probability distribution which introduce the spatial information of images. However, they often lead to over-smoothing. To overcome these drawbacks, we propose a new brain MR image segmentation algorithm based on MRF with image patch by assigning each pixel in the neighborhood with a different weight according to the similarity between image patches. The proposed method can overcome the noise and keep the details of topology and corner regions. Meanwhile, by introducing the KL distance into the prior probability and posterior probability as an entropy penalty, the proposed algorithm could get better segmentation results through smoothing this penalty term. Experimental results show that our algorithm can overcome the impact of noise on the segmentation results adaptively and efficiently, and get accurate segmentation results. © 2014 Acta Automatica Sinica. All rights reserved.