Wavelet Features for Recognition of First Episode of Schizophrenia from MRI Brain Images

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This publication doesn't include Institute of Computer Science. It includes Faculty of Medicine. Official publication website can be found on muni.cz.


Year of publication 2014
Type Article in Periodical
Magazine / Source Radioengineering
MU Faculty or unit

Faculty of Medicine

Web http://www.radioeng.cz/fulltexts/2014/14_01_0274_0281.pdf
Field Neurology, neurosurgery, neurosciences
Keywords schizophrenia; machine learning; neuroimaging; classification; wavelet transform; MRI
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Description Machine learning methods are increasingly used in various fields of medicine, contributing to early diagnosis and better quality of care. These outputs are particularly desirable in case of neuropsychiatric disorders, such as schizophrenia, due to the inherent potential for creating a new gold standard in the diagnosis and differentiation of particular disorders. This paper presents a scheme for automated classification from magnetic resonance images based on multiresolution representation in the wavelet domain. Implementation of the proposed algorithm, utilizing support vector machines classifier, is introduced and tested on a dataset containing 104 patients with first episode schizophrenia and healthy volunteers. Optimal parameters of different phases of the algorithm are sought and the quality of classification is estimated by robust cross validation techniques. Values of accuracy, sensitivity and specificity over 71% are achieved.
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