Kernel-based clustering is supposed to provide a better analysis tool for pattern classification, which implicitly maps input samples to a high-dimensional space for improving pattern separability. For this implicit space map, the kernel trick is believed to elegantly tackle the problem of “curse of dimensionality”, which has actually been more challenging for kernel-based clustering in terms of computational complexity and classification accuracy, which traditional kernelized algorithms cannot effectively deal with. In this paper, we propose a novel kernel clustering algorithm, called KFCM-III, for this problem by replacing the traditional isotropic Gaussian kernel with the anisotropic kernel formulated by Mahalanobis distance. Moreover, a reduced-set represented kernelized center has been employed for reducing the computational complexity of KFCM-I algorithm and circumventing the model deficiency of KFCM-II algorithm. The proposed KFCMIII has been evaluated for segmenting magnetic resonance imaging (MRI) images. For this task, an image intensity inhomogeneity correction is employed during image segmentation process. With a scheme called preclassification, the proposed intensity correction scheme could further speed up image segmentation. The experimental results on public image data show the superiorities of KFCM-III.