Abstract：In the segmentation of brain image,the image quality is often reduced due to the influence of noise or outliers. And traditional fuzzy clustering has some limitations and is easily affected by the initial value,which brings great trouble for doctors to accurately identify and extract brain tissue. Aiming at these problems,an improved fuzzy clustering image segmentation method based on neighborhoods of image pixels constructed by Markov model was proposed. Firstly,the initial clustering center was determined by Genetic Algorithm(AG). Secondly,the expression of the target function was changed,the calculation method of the membership matrix was changed by adding the correction term in the target function and was adjusted by the constraint coefficient. Finally,the Markov Random Field(WRF)was used to represent the label information of the neighborhood pixels,and the maximized conditional probability of Markov random field was used to represent the neighborhood of the pixel,which improves the noise immunity. Experimental results show that the proposed method has good noise immunity,it can reduce the false segmentation rate and has high segmentation accuracy when used to segment brain images. The average accuracy of the segmented image has Jaccard Similarity(JS)index of 82. 76%,Dice index of 90. 45%,and Sensitivity index of 90. 19%. At the same time,the segmentation of brain image boundaries is clearer and the segmented image is closer to the standard segmentation image.
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2020, Vol. 40 
