Combining various types of classifiers and features extracted
from magnetic resonance imaging data in schizophrenia
recognition

J 2015

Combining various types of classifiers and features extracted from magnetic resonance imaging data in schizophrenia recognition

JANOUŠOVÁ, Eva, Daniel SCHWARZ a Tomáš KAŠPÁREK

Základní údaje

Originální název

Combining various types of classifiers and features extracted from magnetic resonance imaging data in schizophrenia recognition

Autoři

JANOUŠOVÁ, Eva (203 Česká republika, garant, domácí), Daniel SCHWARZ (203 Česká republika, domácí) a Tomáš KAŠPÁREK (203 Česká republika, domácí)

Vydání

Psychiatry Research: Neuroimaging, CLARE, ELSEVIER IRELAND LTD, 2015, 0925-4927

Další údaje

Jazyk

angličtina

Typ výsledku

Článek v odborném periodiku

Obor

30000 3. Medical and Health Sciences

Stát vydavatele

Irsko

Utajení

není předmětem státního či obchodního tajemství

Impakt faktor

Impact factor: 2.477

Kód RIV

RIV/00216224:14110/15:00087443

Organizační jednotka

Lékařská fakulta

DOI

http://dx.doi.org/10.1016/j.pscychresns.2015.03.004

UT WoS

000354552900006

Klíčová slova anglicky

Computational neuroanatomy; Classification; Intersubject principal component analysis (isPCA); Modified maximum uncertainty linear; discriminant analysis (mMLDA); Centroid method; Average linkage

Štítky

EL OK

Příznaky

Mezinárodní význam, Recenzováno

Změněno: 9. 7. 2015 14:50, Soňa Böhmová

Anotace

V originále

We investigated a combination of three classification algorithms, namely the modified maximum uncertainty linear discriminant analysis (mMLDA), the centroid method, and the average linkage, with three types of features extracted from three-dimensional T1-weighted magnetic resonance (MR) brain images, specifically MR intensities, grey matter densities, and local deformations for distinguishing 49 first episode schizophrenia male patients from 49 healthy male subjects. The feature sets were reduced using intersubject principal component analysis before classification. By combining the classifiers, we were able to obtain slightly improved results when compared with single classifiers. The best classification performance (81.6% accuracy, 75.5% sensitivity, and 87.8% specificity) was significantly better than classification by chance. We also showed that classifiers based on features calculated using more computation-intensive image preprocessing perform better; mMLDA with classification boundary calculated as weighted mean discriminative scores of the groups had improved sensitivity but similar accuracy compared to the original MLDA; reducing a number of eigenvectors during data reduction did not always lead to higher classification accuracy, since noise as well as the signal important for classification were removed. Our findings provide important information for schizophrenia research and may improve accuracy of computer aided diagnostics of neuropsychiatric diseases.

Návaznosti

NT13359, projekt VaV

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

Investor: Ministerstvo zdravotnictví ČR, Zapojení prvku umělé inteligence do plánování efektivního operačního programu

Citovat

JANOUŠOVÁ, Eva, Daniel SCHWARZ a Tomáš KAŠPÁREK. Combining various types of classifiers and features extracted from magnetic resonance imaging data in schizophrenia recognition. Psychiatry Research: Neuroimaging. CLARE: ELSEVIER IRELAND LTD, 2015, roč. 232, č. 3, s. 237-249. ISSN 0925-4927. Dostupné z: https://dx.doi.org/10.1016/j.pscychresns.2015.03.004.

@article{1305888,
   author = {Janoušová, Eva and Schwarz, Daniel and Kašpárek, Tomáš},
   article_location = {CLARE},
   article_number = {3},
   doi = {http://dx.doi.org/10.1016/j.pscychresns.2015.03.004},
   keywords = {Computational neuroanatomy; Classification; Intersubject principal component analysis (isPCA); Modified maximum uncertainty linear; discriminant analysis (mMLDA); Centroid method; Average linkage},
   language = {eng},
   issn = {0925-4927},
   journal = {Psychiatry Research: Neuroimaging},
   title = {Combining various types of classifiers and features extracted from magnetic resonance imaging data in schizophrenia recognition},
   volume = {232},
   year = {2015}
}

TY  - JOUR
ID  - 1305888
AU  - Janoušová, Eva - Schwarz, Daniel - Kašpárek, Tomáš
PY  - 2015
TI  - Combining various types of classifiers and features extracted from magnetic resonance imaging data in schizophrenia recognition
JF  - Psychiatry Research: Neuroimaging
VL  - 232
IS  - 3
SP  - 237-249
EP  - 237-249
PB  - ELSEVIER IRELAND LTD
SN  - 09254927
KW  - Computational neuroanatomy
KW  - Classification
KW  - Intersubject principal component analysis (isPCA)
KW  - Modified maximum uncertainty linear
KW  - discriminant analysis (mMLDA)
KW  - Centroid method
KW  - Average linkage
N2  - We investigated a combination of three classification algorithms, namely the modified maximum uncertainty linear discriminant analysis (mMLDA), the centroid method, and the average linkage, with three types of features extracted from three-dimensional T1-weighted magnetic resonance (MR) brain images, specifically MR intensities, grey matter densities, and local deformations for distinguishing 49 first episode schizophrenia male patients from 49 healthy male subjects. The feature sets were reduced using intersubject principal component analysis before classification. By combining the classifiers, we were able to obtain slightly improved results when compared with single classifiers. The best classification performance (81.6% accuracy, 75.5% sensitivity, and 87.8% specificity) was significantly better than classification by chance. We also showed that classifiers based on features calculated using more computation-intensive image preprocessing perform better; mMLDA with classification boundary calculated as weighted mean discriminative scores of the groups had improved sensitivity but similar accuracy compared to the original MLDA; reducing a number of eigenvectors during data reduction did not always lead to higher classification accuracy, since noise as well as the signal important for classification were removed. Our findings provide important information for schizophrenia research and may improve accuracy of computer aided diagnostics of neuropsychiatric diseases.
ER  -

JANOUŠOVÁ, Eva, Daniel SCHWARZ a Tomáš KAŠPÁREK. Combining various types of classifiers and features extracted from magnetic resonance imaging data in schizophrenia recognition. \textit{Psychiatry Research: Neuroimaging}. CLARE: ELSEVIER IRELAND LTD, 2015, roč.~232, č.~3, s.~237-249. ISSN~0925-4927. Dostupné z: https://dx.doi.org/10.1016/j.pscychresns.2015.03.004.

Podrobný výpis o publikaci