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1. Identity statement
Reference TypeConference Paper (Conference Proceedings)
Sitesibgrapi.sid.inpe.br
Holder Codeibi 8JMKD3MGPEW34M/46T9EHH
Identifier6qtX3pFwXQZeBBx/H3uPp
Repositorysid.inpe.br/banon/2005/08.03.15.07
Last Update2005:08.03.03.00.00 (UTC) administrator
Metadata Repositorysid.inpe.br/banon/2005/08.03.15.07.47
Metadata Last Update2022:06.14.00.13.08 (UTC) administrator
DOI10.1109/SIBGRAPI.2005.50
Citation KeyMachado:2005:DeHiSp
TitleTrue factor analysis in medical imaging: Dealing with high-dimensional spaces
FormatOn-line
Year2005
Access Date2024, Apr. 28
Number of Files1
Size233 KiB
2. Context
AuthorMachado, Alexei Manso Correa
AffiliationPontifícia Universidade Católica de Minas Gerais
EditorRodrigues, Maria Andréia Formico
Frery, Alejandro César
e-Mail Addressalexei@pucminas.br
Conference NameBrazilian Symposium on Computer Graphics and Image Processing, 18 (SIBGRAPI)
Conference LocationNatal, RN, Brazil
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 ::
2022-06-14 00:13:08 :: administrator -> :: 2005
3. Content and structure
Is the master or a copy?is the master
Content Stagecompleted
Transferable1
Version Typefinaldraft
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. .
Arrangement 1urlib.net > SDLA > Fonds > SIBGRAPI 2005 > True factor analysis...
Arrangement 2urlib.net > SDLA > Fonds > Full Index > True factor analysis...
doc Directory Contentaccess
source Directory Contentthere are no files
agreement Directory Contentthere are no files
4. Conditions of access and use
data URLhttp://urlib.net/ibi/6qtX3pFwXQZeBBx/H3uPp
zipped data URLhttp://urlib.net/zip/6qtX3pFwXQZeBBx/H3uPp
Languageen
Target Filemachadoa_true.pdf
User Groupalexeimachado
administrator
Visibilityshown
5. Allied materials
Next Higher Units8JMKD3MGPEW34M/46R3ED5
8JMKD3MGPEW34M/4742MCS
Citing Item Listsid.inpe.br/sibgrapi/2022/05.05.04.08 5
sid.inpe.br/banon/2001/03.30.15.38.24 2
Host Collectionsid.inpe.br/banon/2001/03.30.15.38
6. Notes
Empty Fieldsarchivingpolicy archivist area callnumber contenttype copyholder copyright creatorhistory descriptionlevel dissemination documentstage edition electronicmailaddress group isbn issn label lineage mark mirrorrepository nextedition notes numberofvolumes orcid organization pages parameterlist parentrepositories previousedition previouslowerunit progress project readergroup readpermission resumeid rightsholder schedulinginformation secondarydate secondarykey secondarymark secondarytype serieseditor session shorttitle sponsor subject tertiarymark type url volume


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