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

J 2014

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

DLUHOŠ, Petr, Daniel SCHWARZ and Tomáš KAŠPÁREK

Basic information

Original name

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

Authors

DLUHOŠ, Petr (203 Czech Republic, belonging to the institution), Daniel SCHWARZ (203 Czech Republic, guarantor, belonging to the institution) and Tomáš KAŠPÁREK (203 Czech Republic, belonging to the institution)

Edition

Radioengineering, SPOLECNOST PRO RADIOELEKTRONICKE INZENYRSTVI, 2014, 1210-2512

Other information

Language

English

Type of outcome

Článek v odborném periodiku

Field of Study

30000 3. Medical and Health Sciences

Country of publisher

Czech Republic

Confidentiality degree

není předmětem státního či obchodního tajemství

References:

URL

Impact factor

Impact factor: 0.653

RIV identification code

RIV/00216224:14110/14:00075409

Organization unit

Faculty of Medicine

UT WoS

000334729400033

Keywords in English

schizophrenia; machine learning; neuroimaging; classification; wavelet transform; MRI

Abstract

V originále

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.

Links

ED3.2.00/08.0144, research and development project

Name: CERIT Scientific Cloud

NT13359, research and development project

Name: Pokročilé metody rozpoznávání MR obrazů mozku pro podporu diagnostiky neuropsychiatrických poruch

Investor: Ministry of Health of the CR

Files attached

EL_Schwarz_Wavelet_2.pdf

Request the author's version of the file

Citovat

DLUHOŠ, Petr, Daniel SCHWARZ and Tomáš KAŠPÁREK. Wavelet Features for Recognition of First Episode of Schizophrenia from MRI Brain Images. Radioengineering. SPOLECNOST PRO RADIOELEKTRONICKE INZENYRSTVI, 2014, vol. 23, No 1, p. 274-281. ISSN 1210-2512.

@article{1181736,
   author = {Dluhoš, Petr and Schwarz, Daniel and Kašpárek, Tomáš},
   article_number = {1},
   keywords = {schizophrenia; machine learning; neuroimaging; classification; wavelet transform; MRI},
   language = {eng},
   issn = {1210-2512},
   journal = {Radioengineering},
   title = {Wavelet Features for Recognition of First Episode of Schizophrenia from MRI Brain Images},
   url = {http://www.radioeng.cz/fulltexts/2014/14_01_0274_0281.pdf},
   volume = {23},
   year = {2014}
}

TY  - JOUR
ID  - 1181736
AU  - Dluhoš, Petr - Schwarz, Daniel - Kašpárek, Tomáš
PY  - 2014
TI  - Wavelet Features for Recognition of First Episode of Schizophrenia from MRI Brain Images
JF  - Radioengineering
VL  - 23
IS  - 1
SP  - 274-281
EP  - 274-281
PB  - SPOLECNOST PRO RADIOELEKTRONICKE INZENYRSTVI
SN  - 12102512
KW  - schizophrenia
KW  - machine learning
KW  - neuroimaging
KW  - classification
KW  - wavelet transform
KW  - MRI
UR  - http://www.radioeng.cz/fulltexts/2014/14_01_0274_0281.pdf
N2  - 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.
ER  -

DLUHOŠ, Petr, Daniel SCHWARZ and Tomáš KAŠPÁREK. Wavelet Features for Recognition of First Episode of Schizophrenia from MRI Brain Images. \textit{Radioengineering}. SPOLECNOST PRO RADIOELEKTRONICKE INZENYRSTVI, 2014, vol.~23, No~1, p.~274-281. ISSN~1210-2512.

Detailed Information on Publication Record