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

Mutaqin Akbar, Arita Witanti, Indah Susilawati


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.


computational acceleration, FCM, GPU, image segmentation


Kadir, A., & Susanto, A. (2013). Teori dan Aplikasi Pengolahan Citra. Yogyakarta: ANDI

Muthukrishnan, R., & Radha, M. (2011). Edge detection techniques for image segmentation. International Journal of Computer Science & Information Technology, 3(6), 259

Wicaksono, Y. Segmentasi Citra Warna Dan Tekstur Menggunakan Fuzzy C-Means Dan Filter Gabor

Saikumar, T., Yugander, P., Sreenivasa, P., & Smitha, B. (2011). Colour based image segmentation using fuzzy c-means clustering. In Proceedings of International Conference on Computer and Software Modeling, Singapore (pp. 180-5)

Cebeci, Z., & Yildiz, F. (2015). Comparison of K-Means and Fuzzy C-Means Algorithms on Different Cluster Structures. Journal of Agricultural Informatics , 6 (3), 13-23

Safitri, Q. U., Huda, A. F., & A., A. S. (2017). Segmentasi Citra Menggunakan Algoritma Fuzzy C-Means (FCM) dan Spatial Fuzzy C-Means (sFCM). Jurnal Kubik , 2 (1), 22-34

Owens, J. D., Houston, M., Luebke, D., Green, S., Stones, J. E., & Phillips, J. C. (2008). GPU Computing. Proceedings of the IEEE , 879-899

Akbar, M., Pranowo, & Suyoto. (2017). Computational Acceleration of Image Inpainting Alternating-Direction Implicit (ADI) Method Using GPU CUDA. The 2017 International Conference on Control, Electronics, Renewable Energy and Communications (ICCEREC) (pp. 186-190). Yogyakarta: Telkom University

Xu, L. (2011). Parallel Computing based on GPGPU using Compute Unified Device Architecture. Royal Institute of Technology (KTH)

Tse, J. J. (2012). Image processing with CUDA.

Labati, R. D., Piuri, V., & Scotti, F. (2011). ALL-IDB: The Acute Lymphoblastic Leukemia Image Database for Image Processing. The 2011 IEEE International Conference on Image Processing (ICIP 2011) (pp. 2045-2048). Brussels: IEEE

Saravanan, C. (2010). Color Image to Grayscale Image Conversion. The 2010 Second International Conference on Computer Engineering and Applications (ICCEA). Bali: Seoul National University

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