The invention discloses a mean shift and neighborhood information based fuzzy C-mean image segmentation method, and mainly solves the problems of low segmentation accuracy and poor robustness of an existing image segmentation method. The method comprises the steps of (1) inputting a to-be-segmented image; (2) calculating a cluster number and an initial cluster center by adopting a mean shift algorithm; (3) performing initialization; (4) calculating a weight of an neighborhood image block in the to-be-segmented image; (5) calculating a weighted fuzzy factor weight of each pixel point in the to-be-segmented image; (6) performing cluster iteration; (7) judging whether an iterative stop condition is met or not; and (8) generating a segmented image. According to the method, the neighborhood information of the image is fully utilized, so that the method is high in noise robustness and the accuracy of image segmentation is greatly improved.