In order to improve the image segmentation effect of the traditional f uzzy C-mean (FCM) algorithm,a robust image segmentation algorithm based on the combination of kernel neighborhood information and adapt i ve non-local mean is proposed.Firstly,the initial clustering segmentation is performed using traditional FCM clustering algorith m,and the clustering center is used as the center of the initial cluster algorit hm.Then,the image is smoothed by the adaptive non-local means algorithm,which avoids the i mage over segmentation.Finally,the objective function for introducing the kernel spatial information is optimally solved,the ite rative clustering center and expression of the degree of membership are obtained ,and finally the pixels are classified and the pixels are further filtered using neighborhood pixels membership.The experimental results show that the improved algorithm under the strong noise interference has go od robustness,and can effectively segment the image information and background information.The peak signal-to-noise ratio (PSNR) of the segmentation results with the proposed algorithm i s also higher.