A method to segment color images based on modified Fuzzy-Possibilistic-C-Means clustering algorithm

Image segmentation denotes a process by which an image is partitioned into non-intersecting regions and each region is homogeneous. Many approaches have been proposed for the color image segmentation. Among these approaches, the clustering methods have been extensively investigated and used. Fuzzy C-Means has been used in image segmentation widely. However, it is not good for the image with noise and it also takes more time for execution. In this paper a new modified Fuzzy Possibilistic C-Means (FPCM) clustering algorithm is proposed for color image segmentation of any type of color images. This new proposed clustering algorithm exhibits the robustness to noise, and also faster as compared to the traditional one. The results of experiments show better robustness of our algorithms to noise than other segmentation algorithms. The resultant segmented images are evaluated using various image quality parameters such as PSNR, execution time and number of iterations & clusters. This new proposed algorithm has been tested with images of various formats, size and resolution and the results are proven to be better.