Publication detail

Classification of First-Episode Schizophrenia Using Wavelet Imaging Features

MARŠÁLOVÁ, K. SCHWARZ, D. PROVAZNÍK, I.

Original Title

Classification of First-Episode Schizophrenia Using Wavelet Imaging Features

English Title

Classification of First-Episode Schizophrenia Using Wavelet Imaging Features

Type

conference paper

Language

en

Original Abstract

This work explores the design and implementation of an algorithm for the classification of magnetic resonance imaging data for computer-aided diagnosis of schizophrenia. Features for classification were first extracted using two morphometric methods: voxel-based morphometry (VBM) and deformation-based morphometry (DBM). These features were then transformed into a wavelet domain using the discrete wavelet transform with various numbers of decomposition levels. The number of features was then reduced by thresholding and subsequent selection by: Fisher's Discrimination Ratio (FDR), Bhattacharyya Distance, and Variances (Var.). A Support Vector Machine with a linear kernel was used for classification. The evaluation strategy was based on leave-one-out cross-validation.

English abstract

This work explores the design and implementation of an algorithm for the classification of magnetic resonance imaging data for computer-aided diagnosis of schizophrenia. Features for classification were first extracted using two morphometric methods: voxel-based morphometry (VBM) and deformation-based morphometry (DBM). These features were then transformed into a wavelet domain using the discrete wavelet transform with various numbers of decomposition levels. The number of features was then reduced by thresholding and subsequent selection by: Fisher's Discrimination Ratio (FDR), Bhattacharyya Distance, and Variances (Var.). A Support Vector Machine with a linear kernel was used for classification. The evaluation strategy was based on leave-one-out cross-validation.

Keywords

Machine learning; neuroimaging; schizophrenia; support vector machines

Released

01.05.2020

Publisher

IOS Press

Location

Geneve

ISBN

978-1-64368-083-5

Book

Digital Personalized Health and Medicine

Edition

Studies in Health Technology and Informatics

Pages from

1221

Pages to

1222

Pages count

2

URL

Documents

BibTex


@inproceedings{BUT167774,
  author="Kateřina {Maršálová} and Daniel {Schwarz} and Ivo {Provazník}",
  title="Classification of First-Episode Schizophrenia Using Wavelet Imaging Features",
  annote="This work explores the design and implementation of an algorithm for the classification of magnetic resonance imaging data for computer-aided diagnosis of schizophrenia. Features for classification were first extracted using two morphometric methods: voxel-based morphometry (VBM) and deformation-based morphometry (DBM). These features were then transformed into a wavelet domain using the discrete wavelet transform with various numbers of decomposition levels. The number of features was then reduced by thresholding and subsequent selection by: Fisher's Discrimination Ratio (FDR), Bhattacharyya Distance, and Variances (Var.). A Support Vector Machine with a linear kernel was used for classification. The evaluation strategy was based on leave-one-out cross-validation.",
  address="IOS Press",
  booktitle="Digital Personalized Health and Medicine",
  chapter="167774",
  doi="10.3233/SHTI200372",
  edition="Studies in Health Technology and Informatics",
  howpublished="online",
  institution="IOS Press",
  year="2020",
  month="may",
  pages="1221--1222",
  publisher="IOS Press",
  type="conference paper"
}