Medical Image Segmentation Based on Improved Fuzzy C-means Clustering

Traditional fuzzy C-means clustering (FCM) algorithm has the problem of large amount of calculation and too long operation time in medical image segmentation. In the case that sample set is not ideal, it can lead to bad clustering results. Against disadvantages of this algorithm, a kind of medical segmentation algorithm is put forward based on hybrid leapfrog optimized fuzzy c-means clustering. The step length of the frog directly affects the performance of the algorithm. Therefore, we propose the appropriate step update strategy. The experiment results show that the proposed algorithm can ensure the continuity of the image edge compared with fuzzy c-means clustering algorithm. It can provide certain reference for the design of real medical image segmentation system.