A Multiple Kernel Fuzzy C-means Clustering Algorithm for Brain Mr Image Segmentation

In spite of its computational efficiency and wide spread popularity, the FCM algorithm does not take the spatial information of pixels into consideration. In this paper, a multiple kernel fuzzy c-means clustering (MKFCM) algorithm is presented for fuzzy segmentation of magnetic resonance (MR) images. By introducing a novel adaptive method to compute the weights of local spatial values in the objective function, the new multiple kernel fuzzy clustering algorithm is capable of utilizing local contextual information to impose local spatial continuity, thus improving the classification accuracy and reduces the number of iterations. To estimate the intensity in homogeneity, the global intensity is introduced into the coherent local intensity clustering algorithm. Our results show that the proposed MKFCM algorithm can effectively segment the test images and MR images. Comparisons with other FCM approaches based on number of iterations and time complexity demonstrate the superior performance of the proposed algorithm.