Progress In Electromagnetics Research B
ISSN: 1937-6472
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By U. Javed, M. M. Riaz, A. Ghafoor, and T. A. Cheema

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A technique for magnetic resonance brain image classification using perceptual texture features, fuzzy weighting and support vector machines is proposed. In contrast to existing literature which generally classify the magnetic resonance brain images into normal and abnormal classes, classification with in abnormal brain which is relatively hard and challenging problem is addressed here. Texture features along with invariant moments are extracted and the weights are assigned to each feature to increase classification accuracy. Multi-class support vector machine is used for classification purpose. Results demonstrate that the classification accuracy of the proposed scheme is better than the state of the art existing techniques.

U. Javed, M. M. Riaz, A. Ghafoor, and T. A. Cheema, "MRI Brain Classification Using Texture Features, Fuzzy Weighting and Support Vector Machine," Progress In Electromagnetics Research B, Vol. 53, 73-88, 2013.

1. Hakyemez, B., C. Erdogan, N. Bolca, N. Yildirim, G. Gokalp, and M. Parlak, "Evaluation of different cerebral mass lesions by perfusion-weighted MR imaging ," Journal of Magnetic Resonance Imaging, Vol. 24, No. 4, 817-824, 2006.

2. Lau, P. Y., F. C. T. Voon, and S. Ozawa, The detection and visualization of brain tumors on T2-weighted MRI images using multiparameter feature blocks, International Conference of the Engineering in Medicine and Biology Society, 5104-5107, Janurary 17-18, 2006.

3. Zhang, Y., S. Wang, and L. Wu, "A novel method for magnetic resonance brain image classification based on adaptive chaotic PSO," Progress In Electromagnetics Research, Vol. 109, 325-343, 2010.

4. Zhang, Y., L. Wu, and S. Wang, "Magnetic resonance brain image classification by an improved artificial bee colony algorithm," Progress In Electromagnetics Research, Vol. 116, 65-79, 2011.

5. Zhang, Y. and L.Wu, "An MR brain images classifier via principal component analysis and kernel support vector machine," Progress In Electromagnetics Research, Vol. 130, 369-388, 2012.

6. El-Dahshan, E.-S., T. Hosny, and A. M. Salem, "Hybrid intelligent techniques for MRI brain images classification," Digital Signal Processing, Vol. 20, No. 2, 433-441, 2010.

7. Das, S., M. Chowdhury, and M. K. Kundu, "Brain MR image classification using multi-scale geometric analysis of ripplet," Progress In Electromagnetics Research, Vol. 137, 1-17, 2013.

8. Malthouse, E. C., "Limitations of nonlinear PCA as performed with generic neural networks," IEEE Transactions on Neural Networks,, Vol. 9, No. 1, 165-173, 1998.

9. Chaplot, S., L. M. Patnaik, and N. R. Jagannathan, "Classification of magnetic resonance brain images using wavelets as input to support vector machine and neural network," Biomedical Signal Processing and Control, Vol. 1, No. 1, 86-92, 2006.

10. Yeh, J.-Y. and J. C. Fu, "A hierarchical genetic algorithm for segmentation of multi-spectral human-brain MRI," Expert Systems with Applications, Vol. 34, No. 2, 1285-1295, 2008.

11. Shelvy, P. T., V. Palanisamy, and T. Purusothaman, "Performance analysis of clustering algorithms in brain tumor detection of MR images," European Journal of Scienti¯c Research, Vol. 62, No. 3, 321-330, 2011.

12. Othman, M. F. and M. A. M. Basri, Probabilistic neural network for brain tumor classification, International Conference on Intelligent Systems, Modelling and Simulation, 136-138, January 25-27, 2011.

13. Joshi, D. M., N. K. Rana, and V. M. Misra, Classification of brain cancer using artificial neural network, International Conference on Electronic Computer Technology, 112-116, May 7-10, 2010.

14. Zacharaki, E. I., S. Wang, S. Chawla, D. S. Yoo, R. Wolf, E. R. Melhem, and C. Davatzikos, MRI-based classification of brain tumor type and grade using SVM-RFE, IEEE International Symposium on Biomedical Imaging: From Nano to Macro, 1035-1038, June 28-31, 2009.

15. Cortes, C. and V. Vapnik, "Support vector networks," Machine Learning, Vol. 20, No. 3, 273-297, 1995.

16. Hu, M. K., "Visual pattern recognition by moment invariants," IRE Transactions on Information Theory, Vol. 8, No. 2, 179-187, 1962.

17. Amadasun, M. and R. King, "Textural features corresponding to textural properties," IEEE Transactions on Systems, Man and Cybernetics, Vol. 19, No. 5, 1264-1274, 1989.

18. Vapnik, V., The Nature of Statistical Learning Theory, Springer, New York, USA, 1995.

19. Javed, U., M. M. Riaz, T. A. Cheema, and H. M. F. Zafar, Detection of lung tumor in CE CT images by using weighted support vector machines , International Bhurban Conference on Applied Sciences and Technology, 113-116.

20. Horng, M. H., "Multi-class support vector machine for classification of the ultrasonic images of supraspinatus," Expert Systems with Applications, Vol. 86, No. 4, 8124-8133, 2009.

21. Anand, A. and P. N. Suganthan, "Multiclass cancer classification by support vector machines with class-wise optimized genes and probability estimates," Journal of Theoretical Biology, Vol. 259, No. 3, 533-540, 2009.

22. Hsu, C. W. and C. J. Lin, "A comparison of methods for multiclass support vector machines," IEEE Transactions on Neural Networks, Vol. 13, No. 2, 415-425, March 2002.

23. Rifkin, R. and A. Klautau, "In defense of one-vs-all classification," The Journal of Machine Learning Research, Vol. 5, 101-141, 2004.

24. Yang, W., H. Xia, B. Xia, L. M. Lui, and X. Huang, ICA-based feature extraction and automatic classification of AD-related MRI data, International Conference on Natural Computation, 1261-1265, August 10-12, 2010.

25. Harvard Medical Atlas Database, http://www.med.harvard.edu/AANLIB/home.html.

26. Arivazhagana, S., L. Ganesanb, and T. G. S. Kumara, "Texture classification using ridgelet transform," Pattern Recognition Letters, Vol. 27, No. 16, 1875-1883, 2006.

27. Riaz, M. M. and A. Ghafoor, "Principle component analysis and fuzzy logic based through wall image enhancement," Progress In Electromagnetic Research, Vol. 127, 461-478, 2012.

28. Riaz, M. M. and A. Ghafoor, "Spectral and textural weighting using Takagi-Sugeno fuzzy system for through wall image enhancement," Progress In Electromagnetic Research B, Vol. 48, 115-130, 2013.

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