Rough Spatial Ensemble Kernelized Fuzzy C Means Clustering for Robust Brain MR Image Tissue Segmentation

Image segmentation is a crucial step in image processing having various applications in biomedical image analysis. Segmentation of the magnetic resonance images of the brain is one such key area in biomedical image analysis that segments various tissues in the brain and detects tumor regions. In this paper, an unsupervised rough spatial ensemble kernelized fuzzy clustering segmentation algorithm is presented for automated segmentation of magnetic resonance images of the brain. The proposed algorithm is an integration of Rough Fuzzy C Means clustering and the kernel method with a novel ensemble kernel being a combination of spherical kernel, Gaussian, and Cauchy kernels, which improves the performance of the segmentation algorithm. The proposed algorithm performs better than the existing clustering algorithms across a wide range of magnetic resonance images of the brain along with visual indications obtained from the results.