Uncertainty parameter weighted entropy-based fuzzy c-means algorithm using complemented membership functions for noisy volumetric brain MR image segmentation

In this paper, we propose an uncertainty parameter weighted entropy-based fuzzy c-means clustering algorithm for noisy volumetric (3D) brain MR image segmentation using complemented global and spatially constraint local membership functions. Due to inherent noise and intensity inhomogeneity (IIH), the acquired MR images have blurry tissue boundaries. This leads to a situation where uncertainty arises while labeling a pixel/voxel into its proper tissue region. Further, it magnifies in the regions of different tissue boundaries. The proposed algorithm addresses this issue by introducing a class-level uncertainty parameter for each voxel and weightedly incorporating in the fuzzy objective function using complemented global and spatially constraint local fuzzy membership functions. It also incorporates total uncertainties in the 3D image domain by means of Shannon entropy. The complemented local fuzzy membership function estimates the degree of non-association, constraint by the local region-level intensity distribution. This framework allows the algorithm to utilize the spatial intensity distribution both in locally and globally within the image domain and produce more accurate cluster prototypes. We validate the proposed algorithm qualitatively and quantitatively with 10 3D simulated brain MR images, contaminated by noise and intensity inhomogeneity, 4 volumes of clinical real patient 3D brain MR images and a synthetic 3D image with added Rician noise. The simulation results in several evaluation indices indicate its superiority over state-of-the-art algorithms developed in recent past.