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

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Publikace nespadá pod Filozofickou fakultu, ale pod Lékařskou fakultu. Oficiální stránka publikace je na webu muni.cz.
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DLUHOŠ Petr SCHWARZ Daniel KAŠPÁREK Tomáš

Rok publikování 2014
Druh Článek v odborném periodiku
Časopis / Zdroj Radioengineering
Fakulta / Pracoviště MU

Lékařská fakulta

Citace
www http://www.radioeng.cz/fulltexts/2014/14_01_0274_0281.pdf
Obor Neurologie, neurochirurgie, neurovědy
Klíčová slova schizophrenia; machine learning; neuroimaging; classification; wavelet transform; MRI
Přiložené soubory
Popis 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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