Data Reduction In Classification Of 3-D Brain Images In The
Schizophrenia Research

D 2010

Data Reduction In Classification Of 3-D Brain Images In The Schizophrenia Research

JANOUŠOVÁ, Eva, Daniel SCHWARZ a Tomáš KAŠPÁREK

Základní údaje

Originální název

Data Reduction In Classification Of 3-D Brain Images In The Schizophrenia Research

Autoři

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

Vydání

Brno, Czech Republic, Analysis of Biomedical Signals and Images, Biosignal-Brno, od s. 69-74, 6 s. 2010

Nakladatel

Brno University of Technology VUT Press

Další údaje

Jazyk

angličtina

Typ výsledku

Stať ve sborníku

Obor

30000 3. Medical and Health Sciences

Stát vydavatele

Česká republika

Utajení

není předmětem státního či obchodního tajemství

Forma vydání

tištěná verze "print"

Odkazy

URL

Kód RIV

RIV/00216224:14110/10:00065559

Organizační jednotka

Lékařská fakulta

ISBN

978-80-214-4106-4

ISSN

UT WoS

000303723700011

Klíčová slova česky

analýza hlavních komponent; klasifikace; MRI; výpočetní neuroanatomie; schizofrenie

Klíčová slova anglicky

Principal Component Analysis; Classification; MRI; Computational Neuroanatomy; Schizophrenia

Příznaky

Mezinárodní význam, Recenzováno

Změněno: 31. 1. 2014 12:38, RNDr. Eva Koriťáková, Ph.D.

Anotace

V originále

Multidimensional image data are usually reduced during preprocessing to lower high computational requirements and to cope with the well-known small sample size problem in the huge data analysis. Two reduction methods based on principal component analysis (PCA) are compared and further modified here to be used in classification of 3-D MRI brain images of first-episode schizophrenia patients and healthy controls. The first reduction method is the two-dimensional principal component analysis (2DPCA) and the second one is the PCA based on covariance matrix of persons (pPCA). The classification efficiency of data reduced by 2DPCA and pPCA are compared while using various input image data and two classification methods – the centroid method and the average linkage method.

Návaznosti

NS10347, projekt VaV

Název: Moderní metody rozpoznávání pro analýzu obrazových dat v neuropsychiatrickém výzkumu

Investor: Ministerstvo zdravotnictví ČR, Moderní metody rozpoznávání pro analýzu obrazových dat v neuropsychiatrickém výzkumu

NS9893, projekt VaV

Název: Predikce průběhu iniciálních fází schizofrenie pomocí morfologie mozku

Investor: Ministerstvo zdravotnictví ČR, Predikace průběhu iniciálních fází schizofrenie pomocí morfologie mozku

Citovat

JANOUŠOVÁ, Eva, Daniel SCHWARZ a Tomáš KAŠPÁREK. Data Reduction In Classification Of 3-D Brain Images In The Schizophrenia Research. In Jan, J; Jirik, R; Kolar, R; Kolarova, J; Kozumplik, J; Provaznik, I. Analysis of Biomedical Signals and Images, Biosignal-Brno. Brno, Czech Republic: Brno University of Technology VUT Press, 2010, s. 69-74. ISBN 978-80-214-4106-4.

@inproceedings{890045,
   author = {Janoušová, Eva and Schwarz, Daniel and Kašpárek, Tomáš},
   address = {Brno, Czech Republic},
   booktitle = {Analysis of Biomedical Signals and Images, Biosignal-Brno},
   editor = {Jan, J; Jirik, R; Kolar, R; Kolarova, J; Kozumplik, J; Provaznik, I},
   keywords = {Principal Component Analysis; Classification; MRI; Computational Neuroanatomy; Schizophrenia},
   howpublished = {tištěná verze "print"},
   language = {eng},
   location = {Brno, Czech Republic},
   isbn = {978-80-214-4106-4},
   pages = {69-74},
   publisher = {Brno University of Technology VUT Press},
   title = {Data Reduction In Classification Of 3-D Brain Images In The Schizophrenia Research},
   url = {http://www.biosignal.cz},
   year = {2010}
}

TY  - JOUR
ID  - 890045
AU  - Janoušová, Eva - Schwarz, Daniel - Kašpárek, Tomáš
PY  - 2010
TI  - Data Reduction In Classification Of 3-D Brain Images In The Schizophrenia Research
PB  - Brno University of Technology VUT Press
CY  - Brno, Czech Republic
SN  - 9788021441064
KW  - Principal Component Analysis
KW  - Classification
KW  - MRI
KW  - Computational Neuroanatomy
KW  - Schizophrenia
UR  - http://www.biosignal.cz
N2  - Multidimensional image data are usually reduced during preprocessing to lower high computational requirements and to cope with the well-known small sample size problem in the huge data analysis. Two reduction methods based on principal component analysis (PCA) are compared and further modified here to be used in classification of 3-D MRI brain images of first-episode schizophrenia patients and healthy controls. The first reduction method is the two-dimensional principal component analysis (2DPCA) and the second one is the PCA based on covariance matrix of persons (pPCA). The classification efficiency of data reduced by 2DPCA and pPCA are compared while using various input image data and two classification methods – the centroid method and the average linkage method.
ER  -

JANOUŠOVÁ, Eva, Daniel SCHWARZ a Tomáš KAŠPÁREK. Data Reduction In Classification Of 3-D Brain Images In The Schizophrenia Research. In Jan, J; Jirik, R; Kolar, R; Kolarova, J; Kozumplik, J; Provaznik, I. \textit{Analysis of Biomedical Signals and Images, Biosignal-Brno}. Brno, Czech Republic: Brno University of Technology VUT Press, 2010, s.~69-74. ISBN~978-80-214-4106-4.

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