Image segmentation is a key step from image processing to image analysis. For the limitation that cluster partitioning has a large dependence on the initial cluster center, an image segmentation model PSOM-K (Particle Swarm Optimization Mutations-K-means) based on improved Particle Swarm Optimization (PSO) algorithm and genetic mutation was proposed. Firstly, the PSO formula was improved by increasing the influence of random neighbor particle positions on its own position, and expanding the search space of the algorithm, so that the algorithm was able to find out the global optimal solution quickly. Secondly, mutation operation of genetic algorithm was combined to improve the generalization ability of the model. Thirdly, the positions of the k-means cluster centers were initialized with the improved PSO algorithm from the three channels: Red (R), Green (G) and Blue (B). Finally, k-means was used to perform the image segmentation from the three channels: R, G, and B, and the images of the three channels were merged. Experimental results on Berkeley Segmentation Dataset (BSDS500) show that the improvement of Feature Similarity Index Measure (FSIM) at k=4 is 7.7% to 12.69% compared to CEFO (Chaotic Electromagnetic Field Optimization) method and 5.05% to 19.02% compared to WOA-DE (Whale Optimization Algorithm-Differential Evolution) method.Compared with the fine-grained segmentation algorithm HWOA (Hybrid Whale Optimization Algorithm), PSOM-K decreases at most 0.45% in FSIM but improves 7.59% to 13.58% in Peak Signal-to-Noise Ratio （PSNR） at k=40. Therefore, three independent channels, increasing the position influence of random neighbor particles in the particle swarm and genetic mutation are three effective strategies to find the better positions of k-means cluster centers, and they can improve the performance of image segmentation greatly.