Wavelet Imaging Features for Classification of First-Episode Schizophrenia

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216224%3A14110%2F19%3A00120073" target="_blank" >RIV/00216224:14110/19:00120073 - isvavai.cz</a>

Result on the web

<a href="https://link.springer.com/chapter/10.1007%2F978-3-030-23762-2_25" target="_blank" >https://link.springer.com/chapter/10.1007%2F978-3-030-23762-2_25</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/978-3-030-23762-2_25" target="_blank" >10.1007/978-3-030-23762-2_25</a>

Result language

angličtina

Original language name

Wavelet Imaging Features for Classification of First-Episode Schizophrenia

Original language description

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.

Czech name

—

Czech description

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Type

D - Article in proceedings

CEP classification

—

OECD FORD branch

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Project

<a href="/en/project/NV17-33136A" target="_blank" >NV17-33136A: Neurominer: unveiling hidden patterns in neuroimaging data</a><br>

Continuities

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Publication year

2019

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Article name in the collection

Information Technology in Biomedicine, ITIB 2019, Kamień Śląski, Poland, 18-20 June, 2019

ISBN

9783030237615

ISSN

2194-5357

e-ISSN

2194-5365

Number of pages

12

Pages from-to

280-291

Publisher name

Springer

Place of publication

Kamień Śląski, Poland

Event location

Kamien Slaski

Event date

Jun 17, 2019

Type of event by nationality

EUR - Evropská akce

UT code for WoS article

000618044200025