Classification of normal and abnormal brain MRI slices using Gabor texture and support vector machines


In computational and clinical environments, autoclassification of brain magnetic resonance image (MRI) slices as normal and abnormal is challenging. The purpose of this study is to investigate the computer vision and machine learning methods for classification of brain magnetic resonance (MR) slices. In routine health-care units, MR scanners are being used to generate a massive number of brain slices, underlying the anatomical details. Pathological assessment from this medical data is being carried out manually by the radiologists or neuro-oncologists. It is almost impossible to analyze each slice manually due to the large amount of data produced by MRI devices at each moment. Irrefutably, if an automated protocol performing this task is executed, not only the radiologist will be assisted, but a better pathological assessment process can also be expected. Numerous schemes have been reported to address the issue of autoclassification of brain MRI slices as normal and abnormal, but accuracy, robustness and optimization are still an open issue. The proposed method, using Gabor filter and support vector machines, classifies brain MRI slices as normal or abnormal. Accuracy, sensitivity, specificity and ROC-curve have been used as standard quantitative measures to evaluate the proposed algorithm. To the best of our knowledge, this is the first study in which experiments have been performed on Whole Brain Atlas-Harvard Medical School (HMS) dataset, achieving an accuracy of 97.5%, sensitivity of 99%, specificity of 92% and ROC-curve as 0.99. To test the robustness against medical traits based on ethnicity and to achieve optimization, a locally developed dataset has also been used for experiments and remarkable results with accuracy (96.5%), sensitivity (98%), specificity (92%) and ROC-curve (0.97) were achieved. Comparison with state-of-the-art methods proved the overall efficacy of the proposed method.

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  1. 1.

    Dmitruk, K., et al.: Sharpening filter for false color imaging of dual-energy X-ray scans. SIViP 11(4), 613–620 (2017)

    Article  Google Scholar 

  2. 2.

    Gilanie, G., et al.: Object extraction from T2 weighted brain MR image using histogram based gradient calculation. Pattern Recogn. Lett. 34(12), 1356–1363 (2013)

    Article  Google Scholar 

  3. 3.

    Bartyzel, K.: Adaptive Kuwahara filter. SIViP 10(4), 663–670 (2016)

    Article  Google Scholar 

  4. 4.

    Tohka, J., Zijdenbos, A., Evans, A.: Fast and robust parameter estimation for statistical partial volume models in brain MRI. Neuroimage 23(1), 84–97 (2004)

    Article  Google Scholar 

  5. 5.

    Speier, W., et al.: Robust skull stripping of clinical glioblastoma multiforme data. In: MICCAI 2011: Medical Image Computing and Computer-Assisted Intervention. Springer, Berlin (2011)

  6. 6.

    Roy, S., et al.: A review on automated brain tumor detection and segmentation from MRI of brain. Int. J. Adv. Res. Comput. Sci. Softw. Eng. 3(6), 1706–1746 (2013)

    MathSciNet  Google Scholar 

  7. 7.

    Cocosco, C.A., Zijdenbos, A.P., Evans, A.C.: A fully automatic and robust brain MRI tissue classification method. Med. Image Anal. 7(4), 513–527 (2003)

    Article  Google Scholar 

  8. 8.

    Attique, M., et al.: Colorization and automated segmentation of human T2 MR brain images for characterization of soft tissues. PloS ONE 7(3), e33616 (2012)

    Article  Google Scholar 

  9. 9.

    Fogel, I., Sagi, D.: Gabor filters as texture discriminator. Biol. Cybern. 61(2), 103–113 (1989)

    Article  Google Scholar 

  10. 10.

    Wallace, G.K.: The JPEG still picture compression standard. IEEE Trans. Consum. Electron. 38(1), 5 (1992)

    Article  Google Scholar 

  11. 11.

    Ito, K., Xiong, K.: Gaussian filters for nonlinear filtering problems. IEEE Trans. Autom. Control 45(5), 910–927 (2000)

    MathSciNet  Article  MATH  Google Scholar 

  12. 12.

    Zhu, H., Chan, F.H., Lam, F.K.: Image contrast enhancement by constrained local histogram equalization. Comput. Vis. Image Underst. 73(2), 281–290 (1999)

    Article  Google Scholar 

  13. 13.

    Haralick, R.M., Shanmugam, K., Dinstein, I.H.: Textural features for image classification. IEEE Trans. Syst. Man Cybern. 3(6), 610–621 (1973)

    Article  Google Scholar 

  14. 14.

    Zhao, Y., et al.: Completed robust local binary pattern for texture classification. Neurocomputing 106, 68–76 (2013)

    Article  Google Scholar 

  15. 15.

    Liu, L., et al.: BRINT: binary rotation invariant and noise tolerant texture classification. IEEE Trans. Image Process. 23(7), 3071–3084 (2014)

    MathSciNet  Article  MATH  Google Scholar 

  16. 16.

    Farokhian, F., et al.: Automatic parameters selection of Gabor filters with the imperialism competitive algorithm with application to retinal vessel segmentation. Biocybern. Biomed. Eng. 37(1), 246–254 (2017)

    Article  Google Scholar 

  17. 17.

    Hearst, M.A., et al.: Support vector machines. IEEE Intell. Syst. Appl. 13(4), 18–28 (1998)

    Article  Google Scholar 

  18. 18.

    Anitha, V., Murugavalli, S.: Brain tumour classification using two-tier classifier with adaptive segmentation technique. IET Comput. Vis. 10(1), 9–17 (2016)

  19. 19.

    El-Dahshan, E.-S.A., et al.: Computer-aided diagnosis of human brain tumor through MRI: a survey and a new algorithm. Expert Syst. Appl. 41(11), 5526–5545 (2014)

    Article  Google Scholar 

  20. 20.

    Saritha, M., Joseph, K.P., Mathew, A.T.: Classification of MRI brain images using combined wavelet entropy based spider web plots and probabilistic neural network. Pattern Recogn. Lett. 34(16), 2151–2156 (2013)

    Article  Google Scholar 

  21. 21.

    Zöllner, F.G., Emblem, K.E., Schad, L.R.: SVM-based glioma grading: optimization by feature reduction analysis. Zeitschrift für medizinische Physik 22(3), 205–214 (2012)

    Article  Google Scholar 

  22. 22.

    El-Dahshan, E.-S.A., Hosny, T., Salem, A.-B.M.: Hybrid intelligent techniques for MRI brain images classification. Digit. Signal Process. 20(2), 433–441 (2010)

    Article  Google Scholar 

  23. 23.

    Ohgaki, H., Kleihues, P.: The definition of primary and secondary glioblastoma. Clin. Cancer Res. 19(4), 764–772 (2013)

    Article  Google Scholar 

  24. 24.

    Herskovits, E.H., Itoh, R., Melhem, E.R.: Accuracy for detection of simulated lesions: comparison of fluid-attenuated inversion-recovery, proton density-weighted, and T2-weighted synthetic brain MR imaging. Am. J. Roentgenol. 176(5), 1313–1318 (2001)

    Article  Google Scholar 

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Correspondence to Usama Ijaz Bajwa.

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Gilanie, G., Bajwa, U.I., Waraich, M.M. et al. Classification of normal and abnormal brain MRI slices using Gabor texture and support vector machines. SIViP 12, 479–487 (2018).

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  • Brain
  • MRI
  • Classification
  • Normal and abnormal brain slices