Gauss-Newton Representation Based Algorithm for Magnetic Resonance Brain Image Classification

  • Lingraj Dora
  • Sanjay Agrawal
  • Rutuparna Panda
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 736)


Brain tumor is a harmful disease worldwide. Every year, a majority of adults as well as children dies due to brain tumor. Early detection of the tumor can enhance the survival rate. Many brain image classification schemes are reported in the literature for early detection of tumors. Thus, it has become a challenging problem in the field of medical image analysis. In this paper, a novel hybrid method is proposed that uses the Gauss-Newton representation based algorithm (GNRBA) with feature selection approach. The proposed method is threefold. Firstly, discrete wavelet transform (DWT) is used as a pre-processing step to extract the features from the brain images. Secondly, principal component analysis (PCA) is used to address the dimensionality problem. Finally, the extracted features in the lower dimensional space are utilized by GNRBA for classification. To show the robustness of the proposed method, real human brain magnetic resonance (MR) images are used to experiment. It is witnessed from the results that the performance of the proposed method is superior as compared to the existing brain image classification methods.


Discrete wavelet transform Principal component analysis Gauss-Newton representation based algorithm 



This work is supported by seed fund grant provided under TEQIP-II, Veer Surendra Sai University of Technology, Burla.


  1. 1.
    Selvanayaki, K., Karnan, M.: CAD system for automatic detection of brain tumor through magnetic resonance image-a review. Int. J. Eng. Sci. Technol. 2(10), 5890–5901 (2010)Google Scholar
  2. 2.
  3. 3.
    Kharrat, A., Benamrane, N., Messaoud, M.B., Abid, M.: Detection of brain tumor in medical images. In: 3rd International Conference on Signals, Circuits and Systems (SCS), Medenine, Tunisia, pp. 1–6 (2009)Google Scholar
  4. 4.
    El-Dahshan, E.S.A., Mohsen, H.M., Revett, K., Salem, A.B.M.: Computer-aided diagnosis of human brain tumor through MRI: a survey and a new algorithm. Expert Syst. Appl. 41(11), 5526–5545 (2014)CrossRefGoogle Scholar
  5. 5.
    El-Dahshan, E.S.A., Hosny, T., Salem, A.B.M.: Hybrid intelligent techniques for MRI brain images classification. Digit. Signal Proc. 20(2), 433–441 (2010)CrossRefGoogle Scholar
  6. 6.
    Ain, Q., Jaffar, M.A., Choi, T.S.: Fuzzy anisotropic diffusion based segmentation and texture based ensemble classification of brain tumor. Appl. Soft Comput. 21, 330–340 (2014)CrossRefGoogle Scholar
  7. 7.
    Al-Kadi, O.S.: A multiresolution clinical decision support system based on fractal model design for classification of histological brain tumours. Comput. Med. Imaging Graph. 41, 67–79 (2015)CrossRefGoogle Scholar
  8. 8.
    Jothi, G., Inbarani, H.H.: Hybrid tolerance rough set-firefly based supervised feature selection for MRI brain tumor image classification. Appl. Soft Comput. 46, 639–651 (2016)CrossRefGoogle Scholar
  9. 9.
    Othman, M.F., Basri, M.A.M.: Probabilistic neural network for brain tumor classification. In: 2nd International Conference on Intelligent Systems, Modelling and Simulation (ISMS), Kuala Lumpur, Malaysia, pp. 136–138 (2011)Google Scholar
  10. 10.
    Sachdeva, J., Kumar, V., Gupta, I., Khandelwal, N., Ahuja, C.K.: A package-SFERCB-Segmentation, feature extraction, reduction and classification analysis by both SVM and ANN for brain tumors. Appl. Soft Comput. 47, 151–167 (2016)CrossRefGoogle Scholar
  11. 11.
    Dora, L., Agrawal, S., Panda, P., Abraham, A.: Optimal breast cancer classification using Gauss-Newton representation based algorithm. Expert Syst. Appl. 85, 134–145 (2017)CrossRefGoogle Scholar
  12. 12.
    Daubechies, I.: Ten Lectures on Wavelets. Society for Industrial and Applied Mathematics, Philadelphia (1992)Google Scholar
  13. 13.
    Hiremath, P.S., Shivashankar, S., Pujari, J.: Wavelet based features for color texture classification with application to CBIR. Int. J. Comput. Sci. Netw. Secur. 6(9A), 124–133 (2006)Google Scholar
  14. 14.
    Zhang, Y., Wang, S., Wu, L.: A novel method for magnetic resonance brain image classification based on adaptive chaotic PSO. Prog. Electromagn. Res. 109, 325–343 (2010)CrossRefGoogle Scholar
  15. 15.
    Messina, A.: Refinements of damage detection methods based on wavelet analysis of dynamical shapes. Int. J. Solids Struct. 45(14), 4068–4097 (2008)CrossRefzbMATHGoogle Scholar
  16. 16.
    Belhumeur, P.N., Hespanha, J.P., Kriegman, D.J.: Eigenfaces vs. fisherfaces: recognition using class specific linear projection. IEEE Trans. Pattern Anal. Mach. Intell. 19(7), 711–720 (1997)CrossRefGoogle Scholar
  17. 17.
    Jang, J.S.R., Sun, C.T., Mizutani, E.: Neuro-Fuzzy and Soft Computing: A Computational Approach to Learning and Machine Intelligence. Prentice-Hall, Englewood Cliffs (1997)Google Scholar
  18. 18.
    Gill, P.E., Murray, W., Wright, M.H.: Practical Optimization, University of Michigan. Academic Press, USA (1981)Google Scholar
  19. 19.
    Fawcett, T.: An introduction to ROC analysis. Pattern Recognit. Lett. 27(8), 861–874 (2006)MathSciNetCrossRefGoogle Scholar
  20. 20.
    Sokolova, M., Lapalme, G.: A systematic analysis of performance measures for classification tasks. Inf. Process. Manag. 45(4), 427–437 (2009)CrossRefGoogle Scholar
  21. 21.
    Hastie, T., Tibshirani, R., Friedman, J.: The Elements of Statistical Learning, 2nd edn. Springer Science & Business Media, New York (2009)CrossRefzbMATHGoogle Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Department of EEEVSSUTBurlaIndia
  2. 2.Department of Electronics and Telecommunication EngineeringVSSUTBurlaIndia

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