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Reference TypeConference Proceedings
Identifier6qtX3pFwXQZG2LgkFdY/LFJ24
Repositorysid.inpe.br/sibgrapi@80/2006/07.07.18.53
Metadatasid.inpe.br/sibgrapi@80/2006/07.07.18.53.28
Sitesibgrapi.sid.inpe.br
Citation KeyThomazAgOlDuBuGiRu:2006:MuLiAp
Author1 Thomaz, Carlos
2 Aguiar, Nelson
3 Oliveira, Sergio
4 Duran, Fabio
5 Busatto, Geraldo
6 Gillies, Duncan
7 Rueckert, Daniel
Affiliation1 Department of Electrical Engineering, Centro Universitario da FEI, São Paulo, Brazil
2 Department of Electrical Engineering, Centro Universitario da FEI, São Paulo, Brazil
3 Department of Electrical Engineering, Centro Universitario da FEI, São Paulo, Brazil
4 Departments of Psychiatry and Radiology, Faculty of Medicine, University of São Paulo, Brazil
5 Departments of Psychiatry and Radiology, Faculty of Medicine, University of São Paulo, Brazil
6 Department of Computing, Imperial College, London, UK
7 Department of Computing, Imperial College, London, UK
TitleExtracting Discriminative Information from Medical Images: A Multivariate Linear Approach
Conference NameBrazilian Symposium on Computer Graphics and Image Processing, 19 (SIBGRAPI)
Year2006
EditorOliveira Neto, Manuel Menezes de
Carceroni, Rodrigo Lima
Book TitleProceedings
Date8-11 Oct. 2006
Publisher CityLos Alamitos
PublisherIEEE Computer Society
Conference LocationManaus
KeywordsStatistical pattern recognition, Medical image computing.
AbstractStatistical discrimination methods are suitable not only for classification but also for characterisation of differences between a reference group of patterns and the population under investigation. In the last years, statistical methods have been proposed to classify and analyse morphological and anatomical structures of medical images. Most of these techniques work in high-dimensional spaces of particular features such as shapes or statistical parametric maps and have overcome the difficulty of dealing with the inherent high dimensionality of medical images by analysing segmented structures individually or performing hypothesis tests on each feature separately. In this paper, we present a general multivariate linear framework to identify and analyse the most discriminating hyper-plane separating two populations. The goal is to analyse all the intensity features simultaneously rather than segmented versions of the data separately or feature-by-feature. The conceptual and mathematical simplicity of the approach, which pivotal step is spatial normalisation, involves the same operations irrespective of the complexity of the experiment or nature of the data, giving multivariate results that are easy to interpret. To demonstrate its performance we present experimental results on artificially generated data set and real medical data.
Languageen
Tertiary TypeFull Paper
FormatOn-line
Size655 KiB
Number of Files1
Target Filethomaz-multivariate.pdf
Last Update2006:07.07.18.56.33 sid.inpe.br/banon/2001/03.30.15.38 administrator
Metadata Last Update2020:02.19.03.17.33 sid.inpe.br/banon/2001/03.30.15.38 administrator {D 2006}
Document Stagecompleted
Is the master or a copy?is the master
e-Mail Addresscet@fei.edu.br
User Groupcethomaz administrator
Visibilityshown
Transferable1
Host Collectionsid.inpe.br/banon/2001/03.30.15.38
Content TypeExternal Contribution
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History2006-07-07 18:56:33 :: cethomaz -> banon ::
2006-08-30 22:00:03 :: banon -> cethomaz ::
2008-07-17 14:11:02 :: cethomaz -> administrator ::
2009-08-13 20:38:00 :: administrator -> banon ::
2010-08-28 20:02:22 :: banon -> administrator ::
2020-02-19 03:17:33 :: administrator -> :: 2006
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Access Date2020, Nov. 28

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