Progress In Electromagnetics Research
ISSN: 1070-4698, E-ISSN: 1559-8985
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By Y.-D. Zhang and L. Wu

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Automated and accurate classification of MR brain images is extremely important for medical analysis and interpretation. Over the last decade numerous methods have already been proposed. In this paper, we presented a novel method to classify a given MR brain image as normal or abnormal. The proposed method first employed wavelet transform to extract features from images, followed by applying principle component analysis (PCA) to reduce the dimensions of features. The reduced features were submitted to a kernel support vector machine (KSVM). The strategy of K-fold stratified cross validation was used to enhance generalization of KSVM. We chose seven common brain diseases (glioma, meningioma, Alzheimer's disease, Alzheimer's disease plus visual agnosia, Pick's disease, sarcoma, and Huntington's disease) as abnormal brains, and collected 160 MR brain images (20 normal and 140 abnormal) from Harvard Medical School website. We performed our proposed methods with four different kernels, and found that the GRB kernel achieves the highest classification accuracy as 99.38%. The LIN, HPOL, and IPOL kernel achieves 95%, 96.88%, and 98.12%, respectively. We also compared our method to those from literatures in the last decade, and the results showed our DWT+PCA+KSVM with GRB kernel still achieved the best accurate classification results. The averaged processing time for a 256x256 size image on a laptop of P4 IBM with 3 GHz processor and 2 GB RAM is 0.0448 s. From the experimental data, our method was effective and rapid. It could be applied to the field of MR brain image classification and can assist the doctors to diagnose a patient normal or abnormal in some degree.

Y.-D. Zhang 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.

1. 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.

2. Mohsin, S. A., N. M. Sheikh, and U. Saeed, "MRI induced heating of deep brain stimulation leads: Effect of the air-tissue interface," Progress In Electromagnetics Research, Vol. 83, 81-91, 2008.

3. Golestanirad, L., A. P. Izquierdo, S. J. Graham, J. R. Mosig, and C. Pollo, "Effect of realistic modeling of deep brain stimulation on the prediction of volume of activated tissue ," Progress In Electromagnetics Research, Vol. 126, 1-16, 2012.

4. Mohsin, S. A., "Concentration of the specific absorption rate around deep brain stimulation electrodes during MRI," Progress In Electromagnetics Research, Vol. 121, 469-484, 2011.

5. Oikonomou, A., I. S. Karanasiou, and N. K. Uzunoglu, "Phased array near field radiometry for brain intracranial applications," Progress In Electromagnetics Research, Vol. 109, 345-360, 2010.

6. Scapaticci, R., L. Di Donato, I. Catapano, and L. Crocco, "A feasibility study on microwave imaging for brain stroke monitoring," Progress In Electromagnetics Research B, Vol. 40, 305-324, 2012.

7. Asimakis, N. P., I. S. Karanasiou, P. K. Gkonis, and N. K. Uzunoglu, "Theoretical analysis of a passive acoustic brain monitoring system," Progress In Electromagnetics Research B, Vol. 23, 165-180, 2010.

8. Chaturvedi, C. M., V. P. Singh, P. Singh, P. Basu, M. Singaravel, R. K. Shukla, A. Dhawan, A. K. Pati, R. K. Gangwar, and S. P. Singh, "2.45 GHz (CW) microwave irradiation alters circadian organization, spatial memory, DNA structure in the brain cells and blood cell counts of male mice, mus musculus," Progress In Electromagnetics Research B, Vol. 29, 23-42, 2011.

9. Emin Tagluk, M., M. Akin, and N. Sezgin, "Classification of sleep apnea by using wavelet transform and artificial neural networks," Expert Systems with Applications, Vol. 37, No. 2, 1600-1607, 2010.

10. Zhang, Y., L. Wu, and G. Wei, "A new classifier for polarimetric SAR images," Progress In Electromagnetics Research, Vol. 94, 83-104, 2009.

11. Camacho, J., J. Picó, and A. Ferrer, "Corrigendum to `The best approaches in the on-line monitoring of batch processes based on PCA: Does the modelling structure matter?' [Anal. Chim. Acta Volume 642 (2009) 59-68]," Analytica Chimica Acta,, Vol. 658, No. 1, 106-106, 2010.

12. 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.

13. Cocosco, C. A., A. P. Zijdenbos, and A. C. Evans, "A fully automatic and robust brain MRI tissue classification method ," Medical Image Analysis, Vol. 7, No. 4, 513-527, 2003.

14. Zhang, Y. and L.Wu, "Weights optimization of neural network via improved BCO approach," Progress In Electromagnetics Research, Vol. 83, 185-198, 2008.

15. 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.

16. Patil, N. S., et al., "Regression models using pattern search assisted least square support vector machines," Chemical Engineering Research and Design, Vol. 83, No. 8, 1030-1037, 2005.

17. Wang, F.-F. and Y.-R. Zhang, "The support vector machine for dielectric target detection through a wall," Progress In Electromagnetics Research Letters, Vol. 23, 119-128, 2011.

18. Xu, Y., Y. Guo, L. Xia, and Y. Wu, "An support vector regression based nonlinear modeling method for Sic mesfet," Progress In Electromagnetics Research Letters, Vol. 2, 103-114, 2008.

19. Li, D., W. Yang, and S. Wang, "Classification of foreign fibers in cotton lint using machine vision and multi-class support vector machine," Computers and Electronics in Agriculture, Vol. 4, No. 2, 274-279, 201.

20. Gomes, T. A. F., et al., "Combining meta-learning and search techniques to select parameters for support vector machines," Neurocomputing, Vol. 75, No. 1, 3-13, 2012.

21. Hable, R., "Asymptotic normality of support vector machine variants and other regularized kernel methods," Journal of Multivariate Analysis, Vol. 106, 92-117, 2012.

22. Ghosh, A., B. Uma Shankar, and S. K. Meher, "A novel approach to neuro-fuzzy classification," Neural Networks, Vol. 22, No. 1, 100-109, 2009.

23. Gabor, D., "Theory of communication. Part 1: The analysis of information," Journal of the Institution of Electrical Engineers Part III: Radio and Communication Engineering, Vol. 93, No. 26, 429-441, 1946.

24. Zhang, Y. and L. Wu, "Crop classification by forward neural network with adaptive chaotic particle swarm optimization," Sensors, Vol. 11, No. 5, 4721-4743, 2011.

25. 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.

26. Ala, G., E. Francomano, and F. Viola, "A wavelet operator on the interval in solving Maxwell's equations," Progress In Electromagnetics Research Letters, Vol. 27, 133-140, 2011.

27. Iqbal, A. and V. Jeoti, "A novel wavelet-Galerkin method for modeling radio wave propagation in tropospheric ducts," Progress In Electromagnetics Research B, Vol. 36, 35-52, 2012.

28. Messina, A., "Refinements of damage detection methods based on wavelet analysis of dynamical shapes," International Journal of Solids and Structures, Vol. 45, No. 14-15, 4068-4097, 2008.

29. Martiskainen, P., et al., "Cow behaviour pattern recognition using a three-dimensional accelerometer and support vector machines," Applied Animal Behaviour Science, Vol. 119, No. 1-2, 32-38, 2009.

30. Bermejo, S., B. Monegal, and J. Cabestany, "Fish age categorization from otolith images using multi-class support vector machines," Fisheries Research, Vol. 84, No. 2, 247-253, 2007.

31. Muniz, A. M. S, et al., "Comparison among probabilistic neural network, support vector machine and logistic regression for evaluating the effect of subthalamic stimulation in Parkinson disease on ground reaction force during gait ," Journal of Biomechanics, Vol. 43, No. 4, 720-726, 2010.

32. Bishop, C. M., Pattern Recognition and Machine Learning (Information Science and Statistics), Springer-Verlag New York, Inc., 2006.

33. Vapnik, V., The Nature of Statistical Learning Theory, Springer-Verlag New York, Inc., 1995.

34. Jeyakumar, V., J. H. Wang, and G. Li, "Lagrange multiplier characterizations of robust best approximations under constraint data uncertainty," Journal of Mathematical Analysis and Applications, Vol. 393, No. 1, 285-297, 2012.

35. Cucker, F. and S. Smale, "On the mathematical foundations of learning," Bulletin of the American Mathematical Society, Vol. 39, 1-49, 2002.

36. Poggio, T. and S. Smale, "The mathematics of learning: Dealing with data," Notices of the American Mathematical Society (AMS), Vol. 50, No. 5, 537-544, 2003.

37. Acevedo-Rodríguez, J., et al., "Computational load reduction in decision functions using support vector machines," Signal Processing, Vol. 89, No. 10, 2066-2071, 2009.

38. Deris, A. M., A. M. Zain, and R. Sallehuddin, "Overview of support vector machine in modeling machining performances," Procedia Engineering, Vol. 24, 308-312, 2011.

39. May, R. J., H. R. Maier, and G. C. Dandy, "Data splitting for artificial neural networks using SOM-based stratified sampling," Neural Networks, Vol. 23, No. 2, 283-294, 2010.

40. Armand, S., et al., "Linking clinical measurements and kinematic gait patterns of toe-walking using fuzzy decision trees," Gait & Posture, Vol. 25, No. 3, 475-484, 2007.

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

42. Evans, A. C., et al., "Brain templates and atlases," NeuroImage, Vol. 62, No. 2, 911-922, 2012.

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