  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 519 seconds for an image dataset with the size of
1MB. While the proposed GPU-based FCM requires only 2.33 seconds for the
similar size of image dataset. An estimated 245-fold speedup is measured for
the data size of 40 KB on a CUDA device that has 448 processors.
