Skip to main content
SpringerLink
Log in

Wavelet Imaging Features for Classification of First-Episode Schizophrenia

  • Conference paper
  • First Online:
Information Technology in Biomedicine (ITIB 2019)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1011))

Included in the following conference series:

  • 536 Accesses

Abstract

Recently, multiple attempts have been made to support computer diagnostics of neuropsychiatric disorders, using neuroimaging data and machine learning methods. This paper deals with the design and implementation of an algorithm for the analysis and classification of magnetic resonance imaging data for the purpose of computer-aided diagnosis of schizophrenia. Features for classification are first extracted using two morphometric methods: voxel-based morphometry (VBM) and deformation-based morphometry (DBM); and then transformed into a wavelet domain by discrete wavelet transform (DWT) with various numbers of decomposition levels. The number of features is reduced by thresholding and subsequent selection by: Fisher’s Discrimination Ratio, Bhattacharyya Distance, and Variances – a metric proposed in the literature recently. Support Vector Machine with a linear kernel is used here as a classifier. The evaluation strategy is based on leave-one-out cross-validation. The highest classification accuracy – 73.08% – was achieved with 1000 features extracted by VBM and DWT at four decomposition levels and selected by Fisher’s Discrimination Ratio and Bhattacharyya distance. In the case of DBM features, the classifier achieved the highest accuracy of 72.12% with 5000 discriminating features, five decomposition levels and the use of Fisher’s Discrimination Ratio.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 169.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 219.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Notes

  1. 1.

    A discrete signal can be called sparse if most of its coefficients equal to zero [22].

References

  1. Anutam, R.: Performance analysis of image denoising with wavelet thresholding methods for different levels of decomposition. Int. J. Multimed. Its Appl. 6(3) (2014)

    Google Scholar 

  2. Bishop, C.: Pattern Recognition and Machine Learning (Information Science and Statistics). Springer, New York (2006). ISBN 9780387310732

    Google Scholar 

  3. Al-Qazzaz, N.K., Bin Mohd Ali, S. H., Ahmad, S. A., Islam, M. S., Escudero, J., : Selection of mother wavelet functions for multi-channel EEG signal analysis during a working memory task. Sensors 15(11), 29015–29035 (2015). Basel, Switzerland

    Google Scholar 

  4. Ashburner, J., Friston, K.J.: Voxel-based morphometry–the methods. NeuroImage 11, 805–821 (2000)

    Article  Google Scholar 

  5. Dluhoš, P.: Multiresolution, feature selection for recognition in magnetic resonance brain images. Master thesis, Masaryk University, Faculty of Science, Department of Experimental Biology, Brno (2013)

    Google Scholar 

  6. Hastie, T., Tibshirani, R., Friedman, J.H.: The Elements of Statistical Learning: Data Mining, Inference, and Prediction. Springer, New York (2009). ISBN 9780387848587

    Google Scholar 

  7. Janousova, E., Montana, G., Kasparek, T., Schwarz, D.: Supervised, multivariate, whole-brain reduction did not help to achieve high classification performance in schizophrenia research. Front. Neurosci. 10 (2016)

    Google Scholar 

  8. Kailath, T.: The divergence and Bhattacharyya distance measures in signal selection. IEEE Trans. Commun. 15(1), 52–60 (1967)

    Article  Google Scholar 

  9. Kašp\(\mathring{{\rm e}}\)k, T., Thomaz, C.E., Sato, J.R., Schwarz, D., Janousova, E., Marecek, R., Prikryl, R., Vanicek, J., Fujita, A., Ceskova, E.: Maximum-uncertainty linear discrimination analysis of first-episode schizophrenia subjects. Psychiatry Res. Neuroimaging 191(3), 174–181 (2011)

    Google Scholar 

  10. Kennedy, K.M., Erickson, K.I., Rodrigue, K.M., Voss, M.W., Colcombe, S.J., Kramer, A.F., Acker, J.D., Raz, N.: Age-related differences in regional brain volumes: a comparison of optimized voxel-based morphometry to manual volumetry. Neurobiol. Aging 30, 1657–1676 (2009)

    Article  Google Scholar 

  11. Kumari, S.: Effect of symlet filter order on denoising of still images. Adv. Comput. Int. J. 3(1), 137–143 (2012)

    Article  MathSciNet  Google Scholar 

  12. Kuncheva, L.I.: Combining Pattern Classifiers: Methods and Algorithms, p. 0471210781. Wiley, ISBN (2004)

    Book  Google Scholar 

  13. Kuncheva, L.I., Rodriguez, J.J., Plumpton, C.O., Linden, D.E.J., Johnston, S.J.: Random subspace ensembles for fMRI classification. IEEE Trans. Med. Imaging 29(2), 531–542 (2010)

    Article  Google Scholar 

  14. Lemm, S., Blankertz, B., Dickhaus, T., Müller, K.-R.: Introduction to machine learning for brain imaging. Neuroimage 56, 387–399 (2011)

    Article  Google Scholar 

  15. Lieberman, J. A., Tollefson, G. D., Charles, C., Zipursky, R., Sharma, T., Kahn, R. S., Group, H. S: Antipsychotic drug effects on brain morphology in first-episode psychosis. Arch. Gen. Psychiatry 62(4), 361–370 (2005)

    Google Scholar 

  16. Mallat, S.: A Wavelet Tour of Signal Processing: The Sparse Way. Elsevier, Boston (2007). ISBN 9780123743701

    Google Scholar 

  17. Misiti, M., Misiti, Y., Oppenheim, G., Poggi, J.-M.: Wavelets and their Applications, p. 9781905209316. Wiley-ISTE, ISBN (2007)

    Book  Google Scholar 

  18. Pierson, R., Magnotta, V.: Long-term antipsychotic treatment and brain volumes: a longitudinal study of first-episode schizophrenia. Arch. Gen. Psychiatry 68(2), 128–137 (2012)

    Google Scholar 

  19. Rathi, V.P.G.P., Palani, S.: Brain tumor MRI image classification with feature selection and extraction using linear discriminant analysis (2012). arXiv:1208.2128 [cs]

  20. Schölkopf, B.: Learning with kernels. J. Electrochem. Soc. 129, 2865 (2002)

    Google Scholar 

  21. Schwarz, D., Kašpárek, T., Provazník, I., Jarkovský, J.: A deformable registration method for automated morphometry of MRI brain images in neuropsychiatric research. IEEE Trans. Med. Imaging 26, 452–461 (2007)

    Article  Google Scholar 

  22. Starck, J.-L., Murtagh, F., Fadili, J.M.: Sparse Image and Signal Processing: Wavelets, Curvelets. Cambridge University Press, Morphological Diversity (2010). ISBN 9780521119139

    Google Scholar 

  23. Vyškovský, R., Schwarz, D., Janoušová, E., Kašpárek, T.: Random subspace ensemble artificial neural networks for first-episode schizophrenia classification. Ann. Comput. Sci. Inf. Syst. 8, 317–321 (2016)

    Google Scholar 

  24. Wang, S., Li, D., Song, X., Wei, Y., Li, H.: A feature selection method based on improved fisher’s discriminant ratio for text sentiment classification. Expert. Syst. Appl. 38(7), 8696–8702 (2011)

    Article  Google Scholar 

  25. Wright, I.C., McGuire, P.K., Poline, J.B., Travere, J.M., Murray, R.M., Frith, C.D., Frackowiak, R.S., Friston, K.J.: A voxel-based method for the statistical analysis of gray and white matter density applied to schizophrenia. Neuroimage 2(4), 244–252 (1995)

    Article  Google Scholar 

  26. Yoon, J.H., Nguyen, D.V., McVay, L.M., Deramo, P., Michael, J., Ragland, J.D., Niendham, T., Solomon, M., Carter, C.S.: Automated classification of fMRI during cognitive control identifies more severely disorganized subjects with schizophrenia. Schizophr. Res. 135, 28–33 (2013)

    Article  Google Scholar 

  27. Zhang, H., Ho, T.B., Lin, M., Liang, X.: Feature extraction for time series classification using discriminating wavelet coefficients. Adv. Neural Netw. 3971, 1394–1399 (2006)

    Google Scholar 

Download references

Acknowledgements

This work was supported by the research grant from the Ministry of Health, Czech Republic No. 17-33136A.

Author information

Authors and Affiliations

  1. Masaryk University, Faculty of Medicine, Kamenice 5, 625 00, Brno-Bohunice, Czech Republic

    Kateřina Maršálová & Daniel Schwarz

  2. Institute of Biostatistics and Analyses, Ltd., Poštovská 68/3, 602 00, Brno, Czech Republic

    Kateřina Maršálová & Daniel Schwarz

Authors
  1. Kateřina Maršálová
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Daniel Schwarz
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding authors

Correspondence to Kateřina Maršálová or Daniel Schwarz .

Editor information

Editors and Affiliations

  1. Faculty of Biomedical Engineering, Silesian University of Technology, Zabrze, Poland

    Ewa Pietka

  2. Faculty of Biomedical Engineering, Silesian University of Technology, Zabrze, Poland

    Pawel Badura

  3. Faculty of Biomedical Engineering, Silesian University of Technology, Gliwice, Poland

    Jacek Kawa

  4. Faculty of Biomedical Engineering, Silesian University of Technology, Zabrze, Poland

    Wojciech Wieclawek

Rights and permissions

Reprints and permissions

Copyright information

© 2019 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Maršálová, K., Schwarz, D. (2019). Wavelet Imaging Features for Classification of First-Episode Schizophrenia. In: Pietka, E., Badura, P., Kawa, J., Wieclawek, W. (eds) Information Technology in Biomedicine. ITIB 2019. Advances in Intelligent Systems and Computing, vol 1011. Springer, Cham. https://doi.org/10.1007/978-3-030-23762-2_25

Download citation

Publish with us

Policies and ethics

Search

Navigation