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Aiming at the problems of poor selection of initial clustering center and poor noise resistance of Fuzzy C-Means (FCM) algorithm in image segmentation, an improved FCM Image segmentation algorithm integrating spatial information is proposed; Firstly, the histogram algorithm and Local Outlier Factor (LOF) algorithm are used to adaptively select the initial clustering center, then the Markov random field is used to obtain the a priori probability to improve the objective function, the method of modifying the membership matrix is used to improve the algorithm flow, and finally the improved algorithm is used for image segmentation; In order to verify the performance of the algorithm, the Berkeley image data set is used as the experimental data, and Dice coefficient, JS coefficient, SA coefficient, PSNR index, running time and iteration times are selected as the evaluation criteria; Experimental results show that the algorithm can obtain better initial clustering centers and has better robustness in dealing with different noisy images.