Multivariate statistical differences of MRI samples of the human brain

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Thomaz C.E.
Duran F.L.S.
Busatto G.F.
Gillies D.F.
Rueckert D.
Journal of Mathematical Imaging and Vision
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THOMAZ, C. E.;Thomaz, Carlos E;Thomaz, Carlos Eduardo;THOMAZ, C.E.;THOMAZ, CARLOS E.;THOMAZ, C.;THOMAZ, CARLOS; DURAN, F. L. S.; BUSATTO FILHO, G.; GILLIES, D. F.; RUECKERT, Daniel. Multivariate Statistical Differences of MRI samples of the Human Brain. Journal of Mathematical Imaging and Vision, v. 29, n. 2, p. 95-106, 2007.
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Multivariate statistical discrimination methods are suitable not only for classification but also for characterization 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 that addresses the small sample size problem in medical images. The goal is to identify and analyse the most discriminating hyper-plane separating two populations using all the intensity features simultaneously rather than segmented versions of the data separately or feature-by-feature. To demonstrate the performance of the multivariate linear framework we carry out experimental results on artificially generated data set and on a real medical data composed of magnetic resonance images (MRI) of subjects suffering from Alzheimer's disease (AD) compared to an elderly healthy control group. To our knowledge this is the first multivariate statistical analysis of the human brain in AD that uses the whole features (texture + shapes) simultaneously rather than segmented version of the images. The conceptual and mathematical simplicity of the approach involves the same operations irrespective of the complexity of the experiment or nature of the spatially normalized data, giving multivariate results that are plausible and easy to interpret by the clinicians. © 2007 Springer Science+Business Media, LLC.