publication . Preprint . 2016

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

Almazrooie, Mishal; Vadiveloo, Mogana; Abdullah, Rosni;
Open Access English
  • Published: 01 Jan 2016
Abstract
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 i...
Subjects
ACM Computing Classification System: ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISIONComputingMethodologies_PATTERNRECOGNITION
free text keywords: Computer Science - Distributed, Parallel, and Cluster Computing, Computer Science - Computer Vision and Pattern Recognition
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26 references, page 1 of 2

[1] Chattopadhyay S., Pratihar D.K., and Sarkar, S.C.D A Comparative Study of Fuzzy C-Means Algorithm and Entropy-Based Fuzzy Clustering Algorithms. Computing and Informatics, Vol. 30, pp. 701-720, 2011

[2] Tou, J.T. and Gonzalez R.C. Pattern Recognition Principles. Addison-Wesley, London.1974

[3] Bezdek J.C. Pattern Recognition with Fuzzy Objective Function Algorithms. Kluwer Academic Publishers. 1981 [OpenAIRE]

[4] Ball G. and Hall D. A Clustering Technique for Summarizing Multivariate Data. Behav Sci 12, pp. 153-155, 1967 [OpenAIRE]

[5] Shen Y. and Li Y-L. An automatic fuzzy c-means algorithm for image segmentation. Springer-Verlag, Vol. 14, Number 2, pp. 123-128. 2009

[6] Vadiveloo M., Abdullah R., Rajeswari M. and Abu-Shareha A.A. Image Segmentation With Cyclic Load Balanced Parallel Fuzzy C-Means Cluster Analysis. IEEE International Conference on Imaging Systems and Techniques, Malaysia, 2011, pp. 124-129. 2011 [OpenAIRE]

[7] Farber Rob, CUDA Application Design and Development. Morgan Kaufmann Publishers Inc. ISPN 9780123884268, San Francisco, CA, USA, 1st edition, 2012.

[8] David B. Kirk and Wen-mei W. Hwu. Programming Massively Parallel Processors: A Hands-on Approach. Morgan Kaufmann Publishers Inc., San Francisco, CA, USA, 1st edition, 2010.

[9] Haiyang Li, Zhaofeng Yang, Hongzhou He. An Improved Image Segmentation Algorithm Based on GPU Parallel Computing. Journal of Software, Vol 9, No 8 (2014), 1985-1990, Aug 2014.

[10] Mahmoud Al-Ayyoub, AnsamM Abu-Dalo, Yaser Jararweh, Moath Jarrah, Mohammad Al Sa'd, A GPU-based implementations of the fuzzy C-means algorithms for medical image segmentation. http: //dx.doi.org/10.1007/s11227-015-1431-y. The Journal of Supercomputing, DOI 10.1007/S11227- 015-1431-Y, ISSN 0920-8542, Pages: 1-14, Springer US, 2015. [OpenAIRE]

[11] Eschrich S., Jingwei Ke, Hall L.O., Goldgof D.B., Fast accurate fuzzy clustering through data reduction. http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=1192702&isnumber=26743. In Fuzzy Systems, IEEE Transactions on Vol.11, No.2, Pages: 262-270, DOI 10.1109/TFUZZ.2003.809902, Apr 2003. [OpenAIRE]

[12] Shalom, S.A.A., Dash, M. and Tue, M. Graphics hardware based e cient and scalable fuzzy c-means clustering. http://dx.doi.org/10.1007/s11227-015-1431-y. In Proceedings of the 7th Australasian data mining conference, volume 87, Australian Computer Society Inc, Darlinghurst, Australia, AusDM 08, pp 179186. 2008.

[13] Rowi ska Z, Gocawski J Cuda based fuzzy c-means acceleration for the segmentation of n images with fungus grown in foam matrices. Image Processing and Communications 17(4):191200. DOI:10.2478/ V10248-012-0046-7, 2012.

t [14] Bernstein A. J. Analysis of Programs for Parallel Processing. Electronic Computers, IEEE Transactions f on , VOL EC-15, NO.5, Pages:757-763, Oct. 1966.

[15] CUDA Array Sum with Reduction, https://gist.github.com/wh5a/4424992. Retrieved 10 May 2015.

26 references, page 1 of 2
Abstract
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 i...
Subjects
ACM Computing Classification System: ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISIONComputingMethodologies_PATTERNRECOGNITION
free text keywords: Computer Science - Distributed, Parallel, and Cluster Computing, Computer Science - Computer Vision and Pattern Recognition
Related Organizations
Download from
26 references, page 1 of 2

[1] Chattopadhyay S., Pratihar D.K., and Sarkar, S.C.D A Comparative Study of Fuzzy C-Means Algorithm and Entropy-Based Fuzzy Clustering Algorithms. Computing and Informatics, Vol. 30, pp. 701-720, 2011

[2] Tou, J.T. and Gonzalez R.C. Pattern Recognition Principles. Addison-Wesley, London.1974

[3] Bezdek J.C. Pattern Recognition with Fuzzy Objective Function Algorithms. Kluwer Academic Publishers. 1981 [OpenAIRE]

[4] Ball G. and Hall D. A Clustering Technique for Summarizing Multivariate Data. Behav Sci 12, pp. 153-155, 1967 [OpenAIRE]

[5] Shen Y. and Li Y-L. An automatic fuzzy c-means algorithm for image segmentation. Springer-Verlag, Vol. 14, Number 2, pp. 123-128. 2009

[6] Vadiveloo M., Abdullah R., Rajeswari M. and Abu-Shareha A.A. Image Segmentation With Cyclic Load Balanced Parallel Fuzzy C-Means Cluster Analysis. IEEE International Conference on Imaging Systems and Techniques, Malaysia, 2011, pp. 124-129. 2011 [OpenAIRE]

[7] Farber Rob, CUDA Application Design and Development. Morgan Kaufmann Publishers Inc. ISPN 9780123884268, San Francisco, CA, USA, 1st edition, 2012.

[8] David B. Kirk and Wen-mei W. Hwu. Programming Massively Parallel Processors: A Hands-on Approach. Morgan Kaufmann Publishers Inc., San Francisco, CA, USA, 1st edition, 2010.

[9] Haiyang Li, Zhaofeng Yang, Hongzhou He. An Improved Image Segmentation Algorithm Based on GPU Parallel Computing. Journal of Software, Vol 9, No 8 (2014), 1985-1990, Aug 2014.

[10] Mahmoud Al-Ayyoub, AnsamM Abu-Dalo, Yaser Jararweh, Moath Jarrah, Mohammad Al Sa'd, A GPU-based implementations of the fuzzy C-means algorithms for medical image segmentation. http: //dx.doi.org/10.1007/s11227-015-1431-y. The Journal of Supercomputing, DOI 10.1007/S11227- 015-1431-Y, ISSN 0920-8542, Pages: 1-14, Springer US, 2015. [OpenAIRE]

[11] Eschrich S., Jingwei Ke, Hall L.O., Goldgof D.B., Fast accurate fuzzy clustering through data reduction. http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=1192702&isnumber=26743. In Fuzzy Systems, IEEE Transactions on Vol.11, No.2, Pages: 262-270, DOI 10.1109/TFUZZ.2003.809902, Apr 2003. [OpenAIRE]

[12] Shalom, S.A.A., Dash, M. and Tue, M. Graphics hardware based e cient and scalable fuzzy c-means clustering. http://dx.doi.org/10.1007/s11227-015-1431-y. In Proceedings of the 7th Australasian data mining conference, volume 87, Australian Computer Society Inc, Darlinghurst, Australia, AusDM 08, pp 179186. 2008.

[13] Rowi ska Z, Gocawski J Cuda based fuzzy c-means acceleration for the segmentation of n images with fungus grown in foam matrices. Image Processing and Communications 17(4):191200. DOI:10.2478/ V10248-012-0046-7, 2012.

t [14] Bernstein A. J. Analysis of Programs for Parallel Processing. Electronic Computers, IEEE Transactions f on , VOL EC-15, NO.5, Pages:757-763, Oct. 1966.

[15] CUDA Array Sum with Reduction, https://gist.github.com/wh5a/4424992. Retrieved 10 May 2015.

26 references, page 1 of 2
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