GPU-Based Fuzzy C-Means Clustering Algorithm for Image Segmentation

In this paper, a fast and practical GPU-based implementation of Fuzzy C-Means (FCM) clustering algorithm for image segmentation is proposed. First, an extensive analysis is conducted to study the dependency among the image pixels in the algorithm for parallelization. The proposed GPU-based FCM has been tested on digital brain simulated dataset to segment white matter(WM), gray matter(GM) and cerebrospinal fluid (CSF) soft tissue regions. The execution time of the sequential FCM is 2798 seconds for an image dataset with the size of 1MB. While the proposed GPU-based FCM requires only 4.2seconds for the similar size of image dataset. An estimated 674-fold superlinear speedup is measured for the data size of 700 KB on a CUDA device that has 448 processors. Superlinear speedup, Fuzzy C-Means, Parallel algorithms, Graphic Processing Units (GPUs), CUDA