Image Segmentation Using Possibilistic C Means Based on Particle Swarm Optimization

Fuzzy c-means has been used in image segmentation widely. However, it isnpsilat better for the image with noise. Possibilistic c means (PCM) clustering algorithm exhibits the robustness to noise, but PCM is very sensitive to initialization and parameter. In this study, in order to avoid the weakness, a novel PCM was presented. It utilizes the strong ability of the global optimizing of the PSO Algorithm, and avoids the sensitivity to local optimization of the FCM algorithm. Furthermore, the PSO defines the centers and numbers of clustering automatically. Two algorithm combined to find a global optimizing clustering. Finally, Applies the crops diseases image, cuts apart the focus from the original image, the experimental result reveals the advantage of the new algorithm lies in the fact that it can not only avoid the coincident cluster problem but also has less noise sensitivity and higher segmentation accuracy.