DTI image segmentation algorithm based on Markov random field and fuzzy C-means clustering

Abstract

The traditional fuzzy C-means clustering (FCM) algorithm only considered the gray information of the image,not including the neighborhood information of it,which would lead to an unsatisfactory anti-noise performance. In order to make full use of image space information,an improved adaptive weighted FCM algorithm combining with Markov random fields (MRF) was proposed in this paper. According to the local density,the discrete types of pixels in the neighborhood of the window were estimated,and the weights of MRF spatial constraint field and membership field were changed adaptively according to the discrete types of pixels,so as to eliminate the influence of noise and maintain the diffusion tensor imaging（DTI）image details as much as possible. The experimental results showed that this algorithm could segment DTI image accurately and achieve the segmentation with clear edge and satisfying detail information reservation. Compared with FCM algorithm and existing MRF and FCM fusion algorithm,the segmentation coefficient was improved by at least 3%,the segmentation entropy was reduced by at least 2%. At the same the segmentation clustering effect was improved,and the segmentation coefficient and the segmentation entropy were not easily affected by the noise amplitude