GPU Accelerated Fuzzy C-Means (FCM) Color Image Segmentation

Abstract

In this paper, computational acceleration of color image segmentation using fuzzy c-means (FCM) algorithm has been presented. The color image is first converted from the Red Green Blue (RGB) color space to the YUV color space. Then, the luma (Y) information values are grouped according to the desired number of clusters using the FCM algorithm. The FCM algorithm is implemented on a Graphical Processing Unit (GPU) using the Compute Unified Device Library (CUDA) library which is developed by NVidia to speed up the computing time. Images used in this research are red blood cell images, geometry images and leaf images. The results of segmented images processed using GPU were seen identic to the results of segmented images processed using the Central Processing Unit (CPU). The computational time of the FCM algorithm can be accelerated by speed-up to 5,628 times faster and the average speed-up of all simulations done is 5,517 times faster.

Image Convolution to Obtain Color ROI after Segmentation Process with Fuzzy Cmeans

“…Fuzzy cmean, is a Fuzzy C-means (FCM) clustering algorithm that allows one part of the data to be in more than one cluster based on the membership function [20]. FCM is a popular segmentation technique used in the field of segmentation [21], [22], [3]. The basic theory of FCM is partitioning of data.…”