MRI Brain Image Segmentation Algorithm Using Watershed Transform and Kernel Fuzzy C-Means Clustering on Level Set Method

A new method for image segmentation is proposed in this paper, which combines the watershed transform, KFCM and level set method. The watershed transform is first used to presegment the image so as to get the initial partition of it. Some useful information of the primitive regions and boundaries can be obtained. The kernel fuzzy c-means (KFCM) was used to generate an initial contour curve which overcomes leaking at the boundary during the curve propagation. KFCM algorithm computes the fuzzy membership values for each pixel. On the basis of KFCM the edge indicator function was redefined. Using the edge indicator function of a MRI image was performed to extract the boundaries of objects on the basis of the presegmentation. Therefore, the proposed method is computationally efficient. Moreover, the algorithm can localize the boundary of the regions exactly due to the edges obtained by the watersheds. The efficiency and accuracy of the algorithm is demonstrated by the experiments on the MR brain images. The above process of segmentation showed a considerable improvement in the evolution of the level set function. KeywordsImage segmentation, Watershed transform, level set method, KFCM, MR brain image. Introduction