2016/01/01 by Mishal Almazrooie, Almazrooie, Mishal, Mogana Vadiveloo +3
Computer Science · Engineering · #Computer Vision and Pattern Recognition (cs.CV) #Distributed #FOS: Computer and information sciences #Image Retrieval and Classification Techniques #Medical Image Segmentation Techniques #Parallel #Remote-Sensing Image Classification #and Cluster Computing (cs.DC)
paper · pdf · doi:10.48550/arxiv.1601.00072
openalex publication_date 2016/01/01 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.