Optimized kernel fuzzy c means (OKFCM) clustering algorithm on level set method for noisy images

In this paper, optimized kernel fuzzy c-means (OKFCM) was used to generate an initial contour curve which overcomes leaking at the boundary during the curve propagation. Firstly, OKFCM algorithm computes the fuzzy membership values for each pixel. On the basis of OKFCM the edge indicator function was redefined. Using the edge indicator function the segmentation of medical images which are added with salt and pepper noise was performed to extract the regions of interest for further processing. The results of the above process of segmentation showed a considerable improvement in the evolution of the level set function.