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Reseach Article

A Comparative Study of K-Means, Fuzzy C-Means and Possibilistic Fuzzy C-Means Algorithm on Noisy Grayscale Images

Published on September 2016 by Sachin Singla, Baljeet Singh Khera
International Conference on Advances in Emerging Technology
Foundation of Computer Science USA
ICAET2016 - Number 1
September 2016
Authors: Sachin Singla, Baljeet Singh Khera
f4bf830b-23ff-4f90-ac33-febbeccdc9f7

Sachin Singla, Baljeet Singh Khera . A Comparative Study of K-Means, Fuzzy C-Means and Possibilistic Fuzzy C-Means Algorithm on Noisy Grayscale Images. International Conference on Advances in Emerging Technology. ICAET2016, 1 (September 2016), 1-5.

@article{
author = { Sachin Singla, Baljeet Singh Khera },
title = { A Comparative Study of K-Means, Fuzzy C-Means and Possibilistic Fuzzy C-Means Algorithm on Noisy Grayscale Images },
journal = { International Conference on Advances in Emerging Technology },
issue_date = { September 2016 },
volume = { ICAET2016 },
number = { 1 },
month = { September },
year = { 2016 },
issn = 0975-8887,
pages = { 1-5 },
numpages = 5,
url = { /proceedings/icaet2016/number1/25875-t017/ },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Proceeding Article
%1 International Conference on Advances in Emerging Technology
%A Sachin Singla
%A Baljeet Singh Khera
%T A Comparative Study of K-Means, Fuzzy C-Means and Possibilistic Fuzzy C-Means Algorithm on Noisy Grayscale Images
%J International Conference on Advances in Emerging Technology
%@ 0975-8887
%V ICAET2016
%N 1
%P 1-5
%D 2016
%I International Journal of Computer Applications
Abstract

Clustering is used to arrange the graphic data in the cluster unsupervised learning methods. Clustering is used in the field of image processing to identifying objects that have same features in an image. Clustering can be categorized into Hard and Fuzzy clustering scheme. This article discusses the study of hard clustering based Standard K-Means and different soft (fuzzy) clustering algorithm exits such as Fuzzy C-Means (FCM) and Possibilistic Fuzzy C-Means (PFCM). These algorithms are used to segment and analyse the standard and coloured images but this research work deals with noisy grayscale images. PSNR, MSE and SSIM are used as evaluation parameter to compare the K-Means, FCM and PFCM results. Finally, the experimental results proved that PFCM favorable over FCM and K-Means.

References
  1. Gonzalez, R. C. , Woods, R. E. and Eddins, S. L. , 2004. Digital image processing using MATLAB.
  2. Fu, K. S. and Mui, J. K. , 1981. A survey on image segmentation. Pattern recognition, 13(1), pp. 3-16.
  3. Pal, N. R. and Pal, S. K. , 1993. A review on image segmentation techniques. Pattern recognition, 26(9), pp. 1277-1294.
  4. Ghosh, S. and Dubey, S. K. , 2013. Comparative analysis of k-means and fuzzy c-means algorithms. International Journal of Advanced Computer Science and Applications, 4(4), pp. 34-39.
  5. Bezdek, J. C. , 2013. Pattern recognition with fuzzy objective function algorithms. Springer Science & Business Media.
  6. Park, D. C. , 2010, April. Intuitive fuzzy C-means algorithm for MRI segmentation. In Information Science and Applications (ICISA), 2010 International Conference on (pp. 1-7). IEEE.
  7. Krishnapuram, R. and Keller, J. M. , 1993. A possibilistic approach to clustering. Fuzzy Systems, IEEE Transactions on, 1(2), pp. 98-110.
  8. Almeida, R. J. and Sousa, J. M. C. , 2006, September. Comparison of fuzzy clustering algorithms for classification. In Evolving Fuzzy Systems, 2006 International Symposium on (pp. 112-117). IEEE.
  9. Pal, N. R. , Pal, K. , Keller, J. M. and Bezdek, J. C. , 2005. A possibilistic fuzzy c-means clustering algorithm. Fuzzy Systems, IEEE Transactions on, 13(4), pp. 517-530.
  10. Santhi, M. V. B. T. , Sai Leela, V. R. N. , Anitha, P. U. and Nagamalleswari, D. , 2011. Enhancing K-Means Clustering Algorithm. International Journal of Computer Science & Technology, IJCST, 2(4), pp. 73-77.
  11. Dehariya, V. K. , Shrivastava, S. K. and Jain, R. C. , 2010, November. Clustering of image data set using K-means and fuzzy K-means algorithms. In Computational Intelligence and Communication Networks (CICN), 2010 International Conference on (pp. 386-391). IEEE.
  12. Quintanilla-Dominguez, J. , Ojeda-Magaña, B. , Cortina-Januchs, M. G. , Ruelas, R. , Vega-Corona, A. and Andina, D. , 2011. Image segmentation by fuzzy and possibilistic clustering algorithms for the identification of microcalcifications. Scientia Iranica, 18(3), pp. 580-589.
  13. Dunn, J. C. , 1973. A fuzzy relative of the ISODATA process and its use in detecting compact well-separated clusters.
  14. Zadeh, L. A. , 1965, Fuzzy sets, Information and control, 8(3), 338-353.
  15. Yang, Y. and Huang, S. , 2012. Image segmentation by fuzzy C-means clustering algorithm with a novel penalty term. Computing and Informatics, 26(1), pp. 17-31.
  16. Pal, N. R. , Pal, K. and Bezdek, J. C. , 1997, July. A mixed c-means clustering model. In Fuzzy Systems, 1997. , Proceedings of the Sixth IEEE International Conference on (Vol. 1, pp. 11-21). IEEE.
  17. AI-Najjar, A. Y. and Soong, D. C. , 2012. Comparison of image quality assessment: PSNR, HVS, SSIM, UIQI. International Journal of Scientific a& Engineering Research, 3(8), p. 1.
Index Terms

Computer Science
Information Sciences

Keywords

Image Segmentation Clustering K-means Fcm Pfcm.