A New Parallel Approach to Fuzzy Clustering for Medical Image Segmentation

Medical image segmentation plays an important role in medical image analysis and visualization. The Fuzzy c-Means (FCM) is one of the well-known methods in the practical applications of medical image segmentation. FCM, however, demands tremendous computational throughput and memory requirements due to a clustering process in which the pixels are classified into the attributed regions based on the global information of gray level distribution and spatial connectivity. In this paper, we present a parallel implementation of FCM using a representative data parallel architecture to overcome computational requirements as well as to create an intelligent system for medical image segmentation. Experimental results indicate that our parallel approach achieves a speedup of 1000x over the existing faster FCM method and provides reliable and efficient processing on CT and MRI image segmentation.