GPU-based parallel fuzzy c-mean clustering model via genetic algorithm

Detection of white matter changes in brain tissue using magnetic resonance imaging has been an increasingly active and challenging research area in computational neuroscience. A genetic algorithm based on a fuzzy c-mean clustering method (GAFCM) was applied to simulated images to separate foreground spot signal information from the background, and the results were compared. The strength of this algorithm was tested by evaluating the segmentation matching factor, coefficient of determination, concordance correlation, and gene expression values. The experimental results demonstrated that the segmentation ability of GAFCM was better than that of fuzzy c-means and K-means algorithms. However, GAFCM is computationally expensive. This study presents a new GPU-based parallel GAFCM algorithm to improve the performance of GAFCM. The experimental results show that computational performance can be increased by a factor of approximately 20 over the CPU-based GAFCM algorithm while maintaining the quality of the processed images. Thus, the proposed GPU-based parallel GAFCM algorithm can achieve the same results and significantly decrease processing time.