We propose an automatic and accurate technique for classifying normal and abnormal magnetic resonance (MR) images of human brain. Ripplet transform Type-I (RT), an efficient multiscale geometric analysis (MGA) tool for digital images, is used to represent the salient features of the brain MR images. The dimensionality of the image representative feature vector is reduced by principal component analysis (PCA). A computationally less expensive support vector machine (SVM), called least square-SVM (LS-SVM) is used to classify the brain MR images. Extensive experiments were carried out to evaluate the performance of the proposed system. Two benchmark MR image datasets and a new larger dataset were used in the experiments, consisting 66, 160 and 255 images, respectively. The generalization capability of the proposed technique is enhanced by 5 × 5 cross validation procedure. For all the datasets used in the experiments, the proposed system shows high classification accuracies (on an average > 99%). Experimental results and performance comparisons with state-of-the-art techniques, show that the proposed scheme is efficient in brain MR image classification.
2. 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.
3. Prasad, P. V., Magnetic Resonance Imaging: Methods and Biologic Applications (Methods in Molecular Medicine), Humana Press, 2005.
4. 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.
5. 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.
6. Maji, P., M. K. Kundu, and B. Chanda, "Second order fuzzy measure and weighted co-occurrence matrix for segmentation of brain MR images," Fundamenta Informaticae, Vol. 88, No. 1-2, 161-176, 2008.
7. 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.
8. 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.
9. Rombouts, S. A., F. Barkhof, and P. Scheltens, Clinical Applications of Functional Brain MRI, Oxford University Press, 2007.
10. 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.
11. Maji, P., B. Chanda, M. K. Kundu, and S. Dasgupta, "Deformation correction in brian MRI using mutual information and genetic algorithm," Proc. Int. Conf. Computing: Theory and Applications, 372-376, 2007.
12. Zhang, Y., L. Wu, and S. Wang, "Magnetic resonance brain image Magnetic resonance brain image classi¯cation by an improved artificial bee colony algorithm," Progress In Electromagnetics Research, Vol. 116, 65-79, 2011.
13. Chaplot, S., L. M. Patnaik, and N. R. Jagannathan, "Classification 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.
14. Maitra, M. and A. Chatterjee, "A Slantlet transform based intelligent system for magnetic resonance brain image classification," Biomedical Signal Processing and Control, Vol. 1, No. 4, 299-306, 2006.
15. 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.
16. 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.
17. Zhang, Y., Z. Dong, L. Wu, and S. Wang, "A hybrid method for MRI brain image classification," Expert Systems with Applications, Vol. 38, No. 8, 10049-10053, 2011.
18. 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.
19. Xu, J., L. Yang, and D. Wu, "Ripplet: A new transform for image processing," Journal of Visual Communication and Image Representation, Vol. 21, No. 7, 627-639, 2010.
20. Candes, E. J. and D. L. Donoho, "Continuous curvelet transform: I. Resolution of the wavefront set," Applied and Computational Harmonic Analysis, Vol. 19, No. 2, 162-197, 2005.
21. Das, S., M. Chowdhury, and M. K. Kundu, "Medical image fusion based on ripplet transform type-I," Progress In Electromagnetics Research B , Vol. 30, 355-370, 2011.
22. Jolliffe, I. T., Principal Component Analysis, Springer, 2002.
23. Suykens, J. A. K. and J. Vandewalle, "Least squares support vector machine classifiers," Neural Processing Letters, Vol. 9, No. 3, 293-300, 1999.