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Aiming at the problem that the existing image segmentation algorithm is complex and the segmentation precision is not high enough, this paper proposed a medical image clustering segmentation algorithm based on geometric distance optimization centroid and rough fuzzy C-means ( RFCM). First it set up the set of pixels represented by the soft set and calculated the distance between each pixel and the centroid. Then it grouped the pixels into clusters based on the minimum distance between the pixel and the centroid. In order to apply the soft set to the coarse fuzzy C-means, it defined a fuzzy soft set. It further converted the input image into a binary image, and selected an appropriate centroid by calculating the geometric distance of the connected region. Finally, using these new centroids to calculate the membership value of the updated pixel, it completed the fuzzy clustering. The performance of the proposed hybrid algorithm was evaluated on three medical databases, such as Allen Brain Atlas. The obtained Jaccard coefficient and segmentation accuracy (SA) were better than several comparison algorithms. Experiments show that the proposed clustering segmentation algorithm has good performance. [ABSTRACT FROM AUTHOR]