CFP last date
20 May 2024
Call for Paper
June Edition
IJCA solicits high quality original research papers for the upcoming June edition of the journal. The last date of research paper submission is 20 May 2024

Submit your paper
Know more
The week's pick
Responsive image
Enhancing Privacy Preservation: Multi-Attribute Protection with P-Sensitive K-Anonymity

Twinkle Patel Kiran Amin
Random Articles
Reseach Article

Performance Analysis of Fuzzy Competitive Learning Algorithms for MR Image Segmentation

by O. Mema Devi, Shahin Ara Begum
journal cover thumbnail

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 71 - Number 9
Year of Publication: 2013
Authors: O. Mema Devi, Shahin Ara Begum
10.5120/12384-8736

O. Mema Devi, Shahin Ara Begum . Performance Analysis of Fuzzy Competitive Learning Algorithms for MR Image Segmentation. International Journal of Computer Applications. 71, 9 ( June 2013), 6-13. DOI=10.5120/12384-8736

@article{ 10.5120/12384-8736,
author = { O. Mema Devi, Shahin Ara Begum },
title = { Performance Analysis of Fuzzy Competitive Learning Algorithms for MR Image Segmentation },
journal = { International Journal of Computer Applications },
issue_date = { June 2013 },
volume = { 71 },
number = { 9 },
month = { June },
year = { 2013 },
issn = { 0975-8887 },
pages = { 6-13 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume71/number9/12384-8736/ },
doi = { 10.5120/12384-8736 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T21:35:05.423556+05:30
%A O. Mema Devi
%A Shahin Ara Begum
%T Performance Analysis of Fuzzy Competitive Learning Algorithms for MR Image Segmentation
%J International Journal of Computer Applications
%@ 0975-8887
%V 71
%N 9
%P 6-13
%D 2013
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Neuro-fuzzy approach have attracted considerable attention in the computational intelligence and segmentation algorithms have been increasingly in developed in improving the accuracy of medical diagnosis. Fuzzy set attempts to represent the human perception whereas neural network attempt to emulate the architecture and information representation scheme of human brain. In this paper a comparative study on the performance of the FCM and the variant fuzzy competitive learning algorithms including the generalized Kohonen's competitive learning (GKCL)-based algorithms (KCL, fuzzy KCL (FKCL), fuzzy soft KCL (FSKCL)) and the learning vector quantization (LVQ)-based algorithms (LVQ, fuzzy LVQ (FLVQ), fuzzy soft LVQ (FSLVQ)) for MR image segmentation is presented. The performance of the algorithms are evaluated using the standard image quality indices such as MSE (mean squared error) and IQI (image quality index) and the results indicate that the soft versions of fuzzy competitive learning algorithms produces more promising results and require less CPU time than the other learning algorithms. Further, the LVQ-based algorithms have better performance according to the values of MSE and IQI as compared to the KCL based algorithms and the FCM algorithm.

References
  1. Vipin Tyagi and J. H. Agarwal, "Medical Image Processing Overview", CSI Communications March, 13-16, 2009.
  2. Anke Meyer Base, "Pattern Recognition for Medical imaging", Academic Press, ISBN: 0-12-493290-8, 2004.
  3. James C. Bezdek, James Keller, Raghu Krisnapuram and Nikhil R. Pal, "Fuzzy Models and Algorithms for Pattern Recognition and Image Processing", Springer 2005.
  4. Karen Chia-Ren Lin, Miin-Shen Yang, Hsiu-Chih Liu, Jiing-Feng Lirng and Pei-Ning Wang, "Generalized Kohonen's competitive learning algorithms for opthalmological MR image segmentation", Magnetic Resonance Imaging 21, 863-870, 2003.
  5. Miin-Shen yang, Karen Chia-Ren Lin, Hsui-Chih Liu and Jiing-Feng Lirng, "Magnetic resonance imaging segmentation techniques using batch-type learning vector quantization algorithms. Magnetic Resonance Imaging 25, 265-277, 2007.
  6. Kuo-Lung Wu and Miin-Shen Yang, "A fuzzy-soft learning vector quantization', Nurocomputing 55, 681-697, 2003.
  7. Suel A. Mingoti and Joab O. Lima, "Comparing SOM neural network with Fuzzy c-means, Kmeans and traditional hierarchical clustering algorithms", European Journal of Operational Research 174, 1742-1759, 2006.
  8. T. Huntsberger, P. Ajjimarangsee, "Parallel self-organizing feature maps for unsupervised pattern recognition", Internat. J. Gen. Systems 16, 357–372, 1990.
  9. Torres Angela and Neito J. J. , "Review Article, Fuzzy Logic in Medicine and Bioinformatics", Hindawi Publishing Corporation, Journal of Biomedicine and Biotechnology, Vol. 2006, Article ID 91908: 1-7, 2006.
  10. Andrea Baraldi and Palma Blonda, "A Survey of Fuzzy Clustering Algorithms for Pattern Recognition-Part I", IEEE Transaction on Systems, Man, and Cybernetics- Part B: Cybernetics Vol. 29 No. 6 December, 778-785, 1999.
  11. Andrea Baraldi and Palma Blonda, "A Survey of Fuzzy Clustering Algorithms for Pattern Recognition-Part II", IEEE Transaction on Systems, Man, and Cybernetics- Part B: Cybernetics Vol. 29 No. 6 December, 786-801, 1999.
  12. George J. Klir and Bo Yuan, "Fuzzy sets and fuzzy logic theory and applications", Prentice Hall of India Private Limited New Delhi-110 001, 2002.
  13. John Yen and Reza Langari, "Fuzzy Logic Intelligence, Control and Information", Pearson Education, Inc. 1999.
  14. J. C. Bezdek, "Pattern recognition with fuzzy objective function algorithms", Plenum, New York, 1981.
  15. Zhou Wang and Alan C. Bovik, "Mean squared error: love it or leave it? A new look at signal fidelity measures", IEEE signal processing magazine 98 January, 98-117, 2009.
  16. Zhou Wang and Alan C. Bovik, "A universal image quality index", IEEE signal processing letters, vol. -9, no. -3, March, 81-84, 2002.
Index Terms

Computer Science
Information Sciences

Keywords

MR image segmentation fuzzy set KCL LVQ

Powered by PhDFocusTM