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In this paper we implemented the algorithm we developed in [1] called 3DPIFCMin a parallel environment by using CUDA on a GPU. In our previous work weintroduced 3DPIFCM which performs segmentation of images in noisy conditionsand uses particle swarm optimization for finding the optimal algorithmparameters to account for noise. This algorithm achieved state of the artsegmentation accuracy when compared to FCM (Fuzzy C-Means), IFCMPSO (ImprovedFuzzy C-Means with Particle Swarm Optimization), GAIFCM (Genetic AlgorithmImproved Fuzzy C-Means) on noisy MRI images of an adult Brain.  When using a genetic algorithm or PSO (Particle Swarm Optimization) on asingle machine for optimization we witnessed long execution times for practicalclinical usage. Therefore, in the current paper our goal was to speed up theexecution of 3DPIFCM by taking out parts of the algorithm and executing them askernels on a GPU. The algorithm was implemented using the CUDA [13] frameworkfrom NVIDIA and experiments where performed on a server containing 64GB RAM , 8cores and a TITAN X GPU with 3072 SP cores and 12GB of GPU memory.  Our results show that the parallel version of the algorithm performs up to27x faster than the original sequential version and 68x faster than GAIFCMalgorithm. We show that the speedup of the parallel version increases as weincrease the size of the image due to better utilization of cores in the GPU.Also, we show a speedup of up to 5x in our Brainweb experiment compared toother generic variants such as IFCMPSO and GAIFCM.

Parallel 3DPIFCM Algorithm for Noisy Brain MRI Images