JANOUŠOVÁ, Eva, Daniel SCHWARZ and 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, p. 69-74. ISBN 978-80-214-4106-4.
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Basic information
Original name Data Reduction In Classification Of 3-D Brain Images In The Schizophrenia Research
Authors JANOUŠOVÁ, Eva (203 Czech Republic, guarantor, belonging to the institution), Daniel SCHWARZ (203 Czech Republic, belonging to the institution) and Tomáš KAŠPÁREK (203 Czech Republic, belonging to the institution).
Edition Brno, Czech Republic, Analysis of Biomedical Signals and Images, Biosignal-Brno, p. 69-74, 6 pp. 2010.
Publisher Brno University of Technology VUT Press
Other information
Original language English
Type of outcome Proceedings paper
Field of Study 30000 3. Medical and Health Sciences
Country of publisher Czech Republic
Confidentiality degree is not subject to a state or trade secret
Publication form printed version "print"
WWW URL
RIV identification code RIV/00216224:14110/10:00065559
Organization unit Faculty of Medicine
ISBN 978-80-214-4106-4
ISSN 1211-412X
UT WoS 000303723700011
Keywords (in Czech) analýza hlavních komponent; klasifikace; MRI; výpočetní neuroanatomie; schizofrenie
Keywords in English Principal Component Analysis; Classification; MRI; Computational Neuroanatomy; Schizophrenia
Tags International impact, Reviewed
Changed by Changed by: RNDr. Eva Koriťáková, Ph.D., učo 184380. Changed: 31/1/2014 12:38.
Abstract
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.
Links
NS10347, research and development projectName: Moderní metody rozpoznávání pro analýzu obrazových dat v neuropsychiatrickém výzkumu
Investor: Ministry of Health of the CR
NS9893, research and development projectName: Predikce průběhu iniciálních fází schizofrenie pomocí morfologie mozku
Investor: Ministry of Health of the CR
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