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Wavelet Imaging Features for Classification of First-Episode Schizophrenia

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


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.


  • Classification
  • Machine learning
  • Neuroimaging
  • Schizophrenia
  • Support vector machines
  • Wavelet transformation

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


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

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Correspondence to Kateřina Maršálová or Daniel Schwarz .

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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.

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