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A Rough Type-2 Fuzzy Clustering Algorithm for MR Image Segmentation

by Shahin Ara Begum, O. Mema Devi
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 54 - Number 4
Year of Publication: 2012
Authors: Shahin Ara Begum, O. Mema Devi
10.5120/8552-2114

Shahin Ara Begum, O. Mema Devi . A Rough Type-2 Fuzzy Clustering Algorithm for MR Image Segmentation. International Journal of Computer Applications. 54, 4 ( September 2012), 4-11. DOI=10.5120/8552-2114

@article{ 10.5120/8552-2114,
author = { Shahin Ara Begum, O. Mema Devi },
title = { A Rough Type-2 Fuzzy Clustering Algorithm for MR Image Segmentation },
journal = { International Journal of Computer Applications },
issue_date = { September 2012 },
volume = { 54 },
number = { 4 },
month = { September },
year = { 2012 },
issn = { 0975-8887 },
pages = { 4-11 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume54/number4/8552-2114/ },
doi = { 10.5120/8552-2114 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T20:54:48.915904+05:30
%A Shahin Ara Begum
%A O. Mema Devi
%T A Rough Type-2 Fuzzy Clustering Algorithm for MR Image Segmentation
%J International Journal of Computer Applications
%@ 0975-8887
%V 54
%N 4
%P 4-11
%D 2012
%I Foundation of Computer Science (FCS), NY, USA
Abstract

As medical images contain uncertainties, there are difficulties in classification of images into homogeneous regions. Fuzzy sets, rough sets and the combination of fuzzy and rough sets plays a prominent role in formalizing uncertainty, vagueness, and incompleteness in diagnosis. Development of hybrid approaches for the segmentation of the magnetic resonance imaging (MRI) with the ability of combining the merits of individual techniques is constantly increasing. The rough-fuzzy c-means (RFCM) clustering algorithm is a hybrid approach combining the merits of fuzzy set and rough set in the c-means framework and is successfully applied to the MR image segmentation. The focus of this paper is on enhancing the computational capability of the fuzzy set based clustering algorithms and the hybrid variant. In this paper we propose an alternative optimal version of the hybrid variant based on type-2 membership function and rough set. We extend and generalized RFCM algorithm with type-2 membership function and call it rough type-2 fuzzy c-means (RT2FCM) algorithm. The RT2FCM algorithm is a generalization of the RFCM algorithm which extends the membership value of each pattern to the type-2 membership functions and is applied to the segmentation of MR images. Experimentation is done using the brain MR images and the results show better detection of abnormal tissues by RT2FCM in contrast to the FCM, T2FCM (Type-2 FCM) and RFCM clustering algorithms.

References
  1. Zadeh L. A. 1965. "Fuzzy Sets. " Information and control 8: 338-353.
  2. Pawlak Z. 1982. "Rough Sets. " International J. Computer and Information Science, vol. 11: 341-356.
  3. Bezdek J. C. 1981. Pattern recognition with fuzzy objective function algorithms. Plenum, New York.
  4. Maji P. and Pal S. K. 2008. "Maximum class separability for Rough-Fuzzy c-means based brain MR image segmentation. " LNCS 5390: 114-134.
  5. Maji P. and Pal S. K. 2007. "Rough set based generalized fuzzy c-means algorithm and quantitative indices. " IEEE Transaction, on system, man and cybernetics, vol. 37, no. 6, 1529-1540.
  6. Peters G. 2006. "Some refinement of rough k-means clustering. " Pattern Recognition 39: 1481-1491.
  7. Zadeh L. A. 1975. "The concept of a linguistic variable and its application to approximate reasoning–1. " Informat. Sci, 8: 199–249.
  8. Base A. M. 2004. Pattern Recognition for Medical Imaging. Academic Press, ISBN: 0-12-493290-8.
  9. Tyagi V. and Agarwal J. H. 2009. "Medical Image Processing Overview. " Computer Society of India (CSI) Communication, 13-16.
  10. Gonzalez R. C. , Woods R. E. and Eddins S. L. 2004. Digital Image Processing Using MATLAB. Pearson Education Inc.
  11. Hall L. O. , Bensaid A. M. , Clark L. P. , Velthuizen R. P. , Silbiger M. S. and Bezdek J. C. 1992. "A Comparison of Neural Network and Fuzzy Clustering Techniques in Segmenting Magnetic Resonance Images of the Brain. " IEEE Transaction on Neural Networks Vol. 3, No. 5, September: 672-682.
  12. Klir G. J. and Yuan B. 2002. Fuzzy sets and fuzzy logic theory and applications. Prentice Hall of India Private Limited New Delhi.
  13. Yen J. and Langari R. 1999. Fuzzy Logic Intelligence, Control and Information. Pearson Education, Inc.
  14. John R. and Coupland S. 2007. "Type-2 Fuzzy Logic: A Historical View. " IEEE Computational Intelligence Magazine February, 1556-603X/07/$25. 00©2007IEEE: 57-62.
  15. Mendel J. M. 2007 (a). "Type-2 Fuzzy Sets and Systems: An Overview. " IEEE Computational Intelligence Magazine February, 1556-603X/07/$25. 00©2007IEEE: 20-29.
  16. Mendel J. M. 2007 (b). "Advances in type-2 fuzzy sets and systems. " Information Sciences 177: 84-110
  17. Kaur P. , Lamba I. M. S. and Gosain A. 2011. "A Novel Kernelized Type-2 Fuzzy c-Means Clustering Algorithm in Segmentation of Noisy Brain Tissues. " IJSTM, Vol. 2 Issue 3, ISSN: 2229-6646, July: 75-84.
  18. Chaira T. 2011. "A novel intuitionistic fuzzy c- means clustering algorithm and its application to medical images. " Applied Soft Computing 11: 1711-1717.
  19. MR image data set, January, . 2012. URL: http://myweb. msoe. edu/~martynsc/images/mri/mri. html.
  20. Wang Z. and Bovik A. C. 2009. "Mean squared error: love it or leave it? A new look at signal fidelity measures. " IEEE signal processing magazine, January, 1053-5888/09/$25. 00©2009IEEE: 98-117.
  21. Wang Z. and Bovik A. C. 2002. "A universal image quality index. " IEEE signal processing letters, vol. 9, no. 3, March: 81-84.
Index Terms

Computer Science
Information Sciences

Keywords

Fuzzy c-means Rough-fuzzy c-means MR imaging image segmentation

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