2010
Two different approaches to small sample size - a common problem in MRI-based studies
JANOUŠOVÁ, Eva; Daniel SCHWARZ and Tomáš KAŠPÁREKBasic information
Original name
Two different approaches to small sample size - a common problem in MRI-based studies
Name in Czech
Srovnání dvou přístupů k problému malého počtu vzorků v MRI studiích
Authors
Edition
Mezinárodní workshop funkční magnetické rezonance, 2010
Other information
Language
English
Type of outcome
Conference abstract
Field of Study
20200 2.2 Electrical engineering, Electronic engineering, Information engineering
Country of publisher
Czech Republic
Confidentiality degree
is not subject to a state or trade secret
Organization unit
Faculty of Medicine
Keywords in English
principal component analysis;schizophrenia;MRI;computational neuroanatomy;2DPCA;pPCA
Changed: 29/3/2010 09:28, RNDr. Eva Koriťáková, Ph.D.
Abstract
In the original language
Recently, the small sample size problem and huge image data have often been discussed in MRI-based studies. Two methods for data reduction are compared here and further modified to solve classification of 3-D MRI data sets in the schizophrenia research. The results show that PCA based on covariance matrix of patients (pPCA) is more suitable for large MRI data reduction than 2DPCA. The results also indicate that deformation images are more appropriate for classification than GM density images.
Links
| NS10347, research and development project |
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| NS9893, research and development project |
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