A rapid knowledge‐based partial supervision fuzzy c‐means for brain tissue segmentation with CUDA‐enabled GPU machine

Abstract

AbstractThe proposed work aims to quicken the magnetic resonance imaging (MRI) brain tissue segmentation process using knowledge‐based partial supervision fuzzy c‐means (KPSFCM) with graphics processing unit (GPU). The proposed KPSFCM contains three steps: knowledge‐based initialization, modification, and optimization. The knowledge‐based initialization step extracts initial centers from input MR images for KPSFCM using Gaussian‐based histogram smoothing. The modification step changes the membership function of PSFCM, which is guided by the labeled patterns of cerebrospinal fluid portion. Finally, the optimization step is achieved through size‐based optimization (SBO), adjacency‐based optimization (ABO), and parallelism‐based optimization (PBO). SBO and ABO are algorithmic level optimization techniques in central processing unit (CPU), whereas PBO is a hardware level optimization technique implemented in GPU using compute unified device architecture (CUDA). Performance of the KPSFCM is tested with online and clinical datasets. The proposed KPSFCM gives better segmentation accuracy than 14 state‐of‐the‐art‐methods, but computationally expensive. When the optimization techniques (SBO and ABO) were included, the execution time reduces by 13 times in CPU. Finally, the inclusion of PBO yields 19 times faster than the optimized CPU implementation.