Abstract

The aim of this study is to present a Computer aided (CAD) system for assisting radiologists in multiclass classification of brain tumors. The diagnosis method consists of four stages pre-processing of MR images, feature extraction, feature reduction and classification. The features are extracted based on discrete wavelet transformation (DWT) using Haarwavele. In the second stage the features of Magnetic resonance images has been reduced using Principal Component analysis(PCA), without degrading the performance of system much. PCA helps in reducing the execution time for classification. In the last stage classification method, Support Vector Machine (SVM) for multi class data is employed. This work is the modification and extension of the previous studies on the diagnosis of brain diseases,to classify tumors in different classes on the basis of location in different parts of brain.

References

• Cancer mortality in India: a nationally representative survey
• S. Chaplot, L.M. Patnaik, N.R. Jagannathan, Classi?cation of magnetic resonance brain images using wavelets as input to support vector machine and neural network, Biomed. Signal Process. Control 1 (2006) 86–92
• M. Maitra, A. Chatterjee, Hybrid multiresolutionSlantlet transform and fuzzy c-means clustering approach for normal-pathological brain MR image segregation, Med. Eng. Phys. (2007), doi:10.1016/j.medengphy.2007.06.009.
• S. Kara, F. Dirgenali, A system to diagnose atherosclerosis via wavelet transforms, principal component analysis and arti?cial neural networks,Expert Syst. Appl. 32 (2007) 632–640
• S.G. Mallat, A theory of multiresolution signal decomposition: the wavelet representation, IEEE Trans. Pattern Anal. Mach. Intell. 11 (7) (1980) 674–693.
• StephaneMallat , Wen Liang Hwang , Singularity detection and processing with wavelets IEEE Trans. Information Theory. Vol.38 No(2) (1992) 621–622. A.K. Jain, Robert P.W. Duin, Jianchang Mao, Statistical pattern recognition: A review, IEEE Trans. Pattern Anal. Mach. Intell., 22 (2000) 4–37.
• R.P. Lippmann, An introduction to computing with neural nets, IEEE Acoustics Speech Signal Processing Mag. 4 (2) (1987) 4–22.
• V.N. Vapnik, Statistical Learning Theory, Wiley, New York, 1998.
• Oppenheim, A. V., Schafer, R. W., Discrete- Time signal Processing, Englewood Cliffs, NJ, Prentice- Hall, 1989.
• K. Karibasappa, S. Patnaik, Face recognition by ANN using wavelet transform coefficients, IE (India) J. Computer Eng. 85 (2004) 17–23.
• El-Sayed Ahmed El-Dahshan a, Tamer Hosny b, Abdel-Badeeh M. Salem “Hybrid intelligent techniques for MRI brain images classification” Digital Signal Processing 20 (2010) 433–441.
• K. Roy, P. Bhattacharya, Optimal features subset selection and classification for Iris recognition, J. Image Video Process. (2008), doi:10.1155/2008/743103
• Jan Lutsa,, Fabian Ojedaa, Raf Van de Plasa,b, Bart De Moora, Sabine Van Huffela, Johan A.K. Suykensa, “A tutorial on support vector machine-based methods for classification problemsin chemometrics” AnalyticaChimicaActa 665 (2010) 129–145