Improving FCM and T2FCM algorithms performance using GPUs for medical images segmentation

Image segmentation gained popularity recently due to numerous applications in many fields such as computer vision, medical imaging. From its name, segmentation is interested in partitioning the image into separate regions where one of them is of special interest. Such region is called the Region of Interest (RoI) and it is very important for many medical imaging problems. Clustering is one of the segmentation approaches typically used on medical images despite its long running time. In this work, we propose to leverage the power of the Graphics Processing Unit (GPU)to improve the performance of such approaches. Specifically, we focus on the Fuzzy C-Means (FCM) algorithm and its more recent variation, the Type-2 Fuzzy C-Means (T2FCM) algorithm. We propose a hybrid CPU-GPU implementation to speed up the execution time without affecting the algorithm's accuracy. The experiments show that such an approach reduces the execution time by up to 80% for FCM and 74% for T2FCM.