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Manaus, AM, Brazil
Oct. 8, 2006 to Oct. 11, 2006
ISBN: 0-7695-2686-1
pp: 113-120
Carlos E. Thomaz , Centro Universitario da FEI, S?o Paulo, Brazil
Nelson A.O. Aguiar , Centro Universitario da FEI, S?o Paulo, Brazil
Sergio H.A. Oliveira , Centro Universitario da FEI, S?o Paulo, Brazil
Fabio L.S. Duran , Faculty of Medicine, University of S?o Paulo, Brazil
Geraldo F. Busatto , Faculty of Medicine, University of S?o Paulo, Brazil
Duncan F. Gillies , Imperial College, London, UK
Daniel Rueckert , Imperial College, London, UK
ABSTRACT
Statistical 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.
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CITATION
Carlos E. Thomaz, Nelson A.O. Aguiar, Sergio H.A. Oliveira, Fabio L.S. Duran, Geraldo F. Busatto, Duncan F. Gillies, Daniel Rueckert, "Extracting Discriminative Information from Medical Images: A Multivariate Linear Approach", SIBGRAPI, 2006, 2012 25th SIBGRAPI Conference on Graphics, Patterns and Images, 2012 25th SIBGRAPI Conference on Graphics, Patterns and Images 2006, pp. 113-120, doi:10.1109/SIBGRAPI.2006.19
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