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Fuzzy Features Selection Technique for Brain MR Images

by Meenakshi, R. B. Dubey
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 81 - Number 6
Year of Publication: 2013
Authors: Meenakshi, R. B. Dubey
10.5120/14015-2061

Meenakshi, R. B. Dubey . Fuzzy Features Selection Technique for Brain MR Images. International Journal of Computer Applications. 81, 6 ( November 2013), 12-23. DOI=10.5120/14015-2061

@article{ 10.5120/14015-2061,
author = { Meenakshi, R. B. Dubey },
title = { Fuzzy Features Selection Technique for Brain MR Images },
journal = { International Journal of Computer Applications },
issue_date = { November 2013 },
volume = { 81 },
number = { 6 },
month = { November },
year = { 2013 },
issn = { 0975-8887 },
pages = { 12-23 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume81/number6/14015-2061/ },
doi = { 10.5120/14015-2061 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T21:55:21.609648+05:30
%A Meenakshi
%A R. B. Dubey
%T Fuzzy Features Selection Technique for Brain MR Images
%J International Journal of Computer Applications
%@ 0975-8887
%V 81
%N 6
%P 12-23
%D 2013
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Brain tumors are the second leading cause of cancer deaths in human throughout the world. Therefore accurate diagnosis is important for successful treatment of brain tumor. When modeling a complex, poorly defined problem with hundreds of possible inputs one must identify the significant inputs before any known modeling techniques can be applied. As generally the data contains many redundant features in it. Redundant features are those which provide no more information than the selected features and irrelevant features provide no more useful information in any context. Feature selection is the process in which one can select a subset of relevant features for use in model construction. Features selection techniques are very useful because they provide: improved model interpretability, shorter training times and enhanced generalization by reducing over fitting. For offering these benefits features selection techniques have become an apparent need in many bio-informatics applications such as brain MR images. In order to get best information a new fuzzy feature selection approach for MR brain tumor image have been used for the first time. The proposed method presents the two stages fuzzy curves and fuzzy surfaces. Fuzzy curves are used to isolate significant features from input and fuzzy surfaces to eliminate input features dependent on the significant input features. Results show that this method is able to achieve a very good reduction in the number of features.

References
  1. Brain Tumor Facts – ABTA, available at www. abta. org/news/brain-tumor-fact-sheets
  2. F. C. Morabito, "A fuzzy neural approach to localizing holes in conducting plate", IEEE Transactions on Magnetics, Vol. 37, No. 5, 2001.
  3. H. Narazaki, and A. L. Ralescu, "An improved synthesis method for multilayered neural networks using qualitative knowledge," IEEE Trans. Fuzzy System. , Vol. 1, No. 2, pp. 125–137, 1993.
  4. SPP11 statistics analysis tutorial. Beijing Hope Electronics Pess, Beijing, 2002.
  5. S. Winkler, M. Affenzeller, and S. Wagner, "Genetic programming based model structure identification using on-line system data", Conceptual Modeling and Simulation Conference – CMS, , Marseilles, France Oct. 2005.
  6. Yinghua Lin, and G. Cunningham, "A fuzzy approach to input variable identification", Proceeding of 3rd IEEE International Conference on. Fuzzy Systems, Orlando, pp. 2031-2036, Jun. 1994.
  7. S. Javeed Hussain, T. Satya Savithri, P. V Sree Devi, "Segmentation of tissues in brain MR images using dynamic neuro-fuzzy technique", IJSCE,vol. -I, issue-6, January 2012.
  8. Indah Soesanti, Adhi Susanto, Thomas Sri Widodo, Maesadji Tjokronagoro, "Optimized fuzzy logic application for MRI brain images segmentation", International Journal of Computer Science and Informational Technology (IJCSIT) vol. 3, No. 5, Oct 2011.
  9. Parmida Moradi Birgani, Meghdad Ashtiyani, Saeed Asadi, "MRI segmentation using fuzzy C-means clustering algorithm basis neural network", Tehran, Iran. ] Information and Communication Technologies: From Theory to Applications, 2008. ICTTA 2008.
  10. ZHANG Nan, "Feature selection based segmentation of multi-Source images: application to brain tumor segmentation in multi-sequence MRI", © [N. Zhang],
  11. , INSA de Lyon, tous droits reserves
  12. J. Jiang, P. Trundle?, J. Ren, "Medical image analysis with artificial neural networks", Computerized Medical Imaging and Graphics 34 (2010) 617–631.
  13. D. Dumitrescu, B. Lazzerini, and L. Jair, "Fuzzy sets and their application to clustering and training", ser. international series on computational intelligence. CBC Press, 2000.
  14. W. Pedrycz, Fuzzy modelling: paradigms and practice. kluwer academic press, 1996.
  15. M. Sugeno and T. Yasukawa, "A fuzzy-logic-based approach to qualitative modeling,", IEEE Trans. Fuzzy Systems, vol. 1, no. 1, 1993, 7-31.
  16. H. Takagi, I. Hayashi, "-driven fuzzy reasoning," 1nt '1. J. approximater, vol. 5, no. 3, 1991, 191-212.
  17. J. B. Antoine Maintz, Petra A. van den Elsen and Max A. Viergever, "Registration of SPECT and MR brain images using a fuzzy surface", Radiological Science Laboratory, Stanford University School of Medicine, Stanford, CA, USA.
  18. Maria-Elena Algorri and Fernando Flores-Mangas,"Classification of anatomical structures in MR brain images using fuzzy parameters",IEEE trans. VOL-51, Sept. 2004.
  19. M. E. Cintra, H. A. Camargo, and M. C. Monard, "A study on techniques for the automatic generation of membership functions for pattern recognition," C3N - Congresso da Academia Trinacional de Ciˆencias, vol. 1, pp. 1–10, 2008.
  20. T. W. Liao, A. K. Celmins, and R. J. Hammell, "A fuzzy C-Means variant for the generation of fuzzy term sets," Fuzzy Sets and Fuzzy Systems, vol. 135, pp. 241–257, 1997.
  21. S. R. Jang, C. T. Sun, and E. Mizutani, Neuro-fuzzy and Soft computing. Prentice Hall, 1997.
  22. O. Cord´on, F. A. C. Gomide, F. Herrera, F. Hoffmann, and L. Magdalena, "Special issue on genetic fuzzy systems," Fuzzy Sets and Systems, vol. 141, 2004.
  23. H. Ishibuchi and Y. Nojima, "Evolutionary multiobjective optimization for the design of fuzzy rule-based ensemble classifiers", International Journal of Hybrid Intelligent Systems, vol. 3, pp. 129–145, 2006.
  24. O. Cord´on, R. Alcal´a, J. Alcal´a-Fdez, and I. Rojas, "Special section on genetic fuzzy systems," IEEE Transactions on Fuzzy Systems, vol. 15, pp. 533–592, 2007.
  25. F. Hoffmann, "Combining boosting and evolutionary algorithms for learning of fuzzy classification rules," Fuzzy Sets and Systems, vol. 141, pp. 47–58, 2004.
  26. P. A. D. Castro and H. A. Camargo, "Focusing on interpretability and accuracy of genetic fuzzy systems," IEEE International Conference on Fuzzy Systems, vol. 1, pp. 696–701, 2005.
  27. M. E. Cintra, H. de Arruda Camargo, and M. C. Monard, "Fuzzy feature subset selection using the Wang & Mendel method", Hybrid Intelligent Systems - HIS 2008, vol. 1, 2008.
  28. L. Wang, "The WM method completed: a flexible fuzzy system approach to data mining," IEEE International Conference on Fuzzy Systems, vol. 11, pp. 768–782, 2003.
  29. H. P. Chan, K. Doi, and C. J. Vyborny. "Improvements in radiologists' detection of clustered micro-calcifications on mammograms: the potential of computer aided diagnosis". Investigative Radiology, 25:1102{1110, 1990.
  30. David A. Clausi, "An analysis of co-occurrence texture statistics as a function of gray level quantization", Can. J. Remote Sensing, Vol. 28, No. 1, pp. 45-62, 2002.
  31. C. Callins Christiyana, V. Rajamani and A. Usha devi, "Ultra sound kidney image retrieval using time efficient one dimensional GLCM texture feature" , Special Issue of International Journal of Computer Applications (0975 – 8887) on Advanced Computing and Communication Technologies for HPC Applications - ACCTHPCA, June 2012.
  32. David A. Clausi," An analysis of co-occurrence texture statistics as a function of grey level quantization", Can. J. Remote Sensing, Vol. 28, No. 1, pp. 45–62, 2002.
  33. Yinghua. Lin, G. Cunningham, S. V. Coggeshall, and R. D. Jone, "Nonlinear system input structure identification: two stage fuzzy curves and surfaces", IEEE Transactions on System, Man, and Cybernetics—Part A: System and Humans, Vol. 28, No. 5, pp. 678-684, Sept. 1998.
  34. Yan-cheng zhang, Han-ping mao, Bo hu, Ming-xi li1, "Features Selection Of Cotton Disease Leaves Image Based On Fuzzy Feature Selection Techniques", Proceedings of the 2007 International Conference on Wavelet Analysis and Pattern Recognition, Beijing, China, 2-4 Nov. 2007-4244-1066-5.
  35. Panagiotis. T. , Stelios E. P. , and Dimitris M. ,"Plant leaves classification based on morphological features and a fuzzy surface selection technique", proceeding of 5th ICTA, Thessaloniki, Greece, pp. 365-370, Oct. 2005.
  36. Bin Li, "Fuzzy approach to identification of input variable", Journal of Harbin Engineering University, Vol. 24 No. 1, pp. 104-107, Feb. 2003 (in Chinese).
  37. National Cancer Data Base (NCDB) - American College of Surgeons, available at http://www. facs. org/cancer/ncdb/
  38. PDQ® - NCI's Comprehensive Cancer Database - National Cancer Institute, available at: http://www. cancer. gov/cancertopics/pdq/cancerdatabase
Index Terms

Computer Science
Information Sciences

Keywords

Fuzzy Curves Fuzzy Surfaces Features Selection Image Segmentation.

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