Identity statement area
Reference TypeConference Paper (Conference Proceedings)
Last Update2005: (UTC) administrator
Metadata Last Update2020: (UTC) administrator
Citation KeyMachado:2005:DeHiSp
TitleTrue factor analysis in medical imaging: Dealing with high-dimensional spaces
Access Date2021, Dec. 07
Number of Files1
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AuthorMachado, Alexei Manso Correa
AffiliationPontifícia Universidade Católica de Minas Gerais
EditorRodrigues, Maria Andréia Formico
Frery, Alejandro César
Conference NameBrazilian Symposium on Computer Graphics and Image Processing, 18 (SIBGRAPI)
Conference LocationNatal
Date9-12 Oct. 2005
PublisherIEEE Computer Society
Publisher CityLos Alamitos
Book TitleProceedings
Tertiary TypeFull Paper
History (UTC)2008-07-17 14:11:01 :: alexeimachado -> banon ::
2008-08-26 15:17:03 :: banon -> administrator ::
2009-08-13 20:37:59 :: administrator -> banon ::
2010-08-28 20:01:20 :: banon -> administrator ::
2020-02-19 03:19:23 :: administrator -> :: 2005
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Is the master or a copy?is the master
Content Stagecompleted
Content TypeExternal Contribution
KeywordsMedical imaging
image registration
factor analysis
AbstractThis article presents a new method for discovering hidden patterns in high-dimensional dataset resulting from image registration. It is based on true factor analysis, a statistical model that aims to find clusters of correlated variables. Applied to medical imaging, factor analysis can potentially identify regions that have anatomic significance and lend insight to knowledge discovery and morphometric investigations related to pathologies. Existent factor analytic methods require the computation of the sample covariance matrix and are thus limited to low-dimensional variable spaces. The proposed algorithm is able to compute the coefficients of the model without the need of the covariance matrix, expanding its spectrum of applications. The method's efficiency and effectiveness is demonstrated in a study of volumetric variability related to the Alzheimer's disease. .
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