Difference between revisions of "TIBM: Group comparison"
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− | Group comparison consists in highlighting the differences between two populations of individuals, with the objective to develop new knowledge in neuroscience. The problem of comparing an individual with a normal model (atlas) is also addressed | + | Group comparison consists in highlighting the differences between two populations of individuals, with the objective to develop new knowledge in neuroscience. The problem of comparing an individual with a normal model (atlas) is also addressed in order to identify pathological areas of a given subject. |
* Thus, work was conducted to demonstrate the advantages of multivariate statistical tests on 2nd order tensor for group study in diffusion MRI [https://icube-publis.unistra.fr/2-BNHLxx 2-BNHLxx]. | * Thus, work was conducted to demonstrate the advantages of multivariate statistical tests on 2nd order tensor for group study in diffusion MRI [https://icube-publis.unistra.fr/2-BNHLxx 2-BNHLxx]. |
Revision as of 11:28, 10 October 2016
Group comparison consists in highlighting the differences between two populations of individuals, with the objective to develop new knowledge in neuroscience. The problem of comparing an individual with a normal model (atlas) is also addressed in order to identify pathological areas of a given subject.
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PhD thesis
- A. Bouchon, Modèles de régression multivariés pour la comparaison de populations en IRM de diffusion, septembre 2016
- T. Gkamas, Statistical modelling of high order tensors in diffusion weighted magnetic resonance imaging, septembre 2015
- G. Sfikas, Modèles statistiques non linéaires pour l'analyse de formes. Application à l'imagerie cérébrale, septembre 2012
- A. Belghith, Indexation de spectres HSQC et d’images IRMf appliquée à la détection de bio-marqueurs, mars 2012
- F. Renard, Création et utilisation d'atlas en IRM de diffusion. Application à l'étude des troubles de la conscience, septembre 2011
- M. Brucher, Représentations compactes et apprentissage non supervisé de variétés non linéaires : application au traitement d’images, octobre 2008
- T. Vik, Modèles statistiques d'apparence non gaussiens. Application à la création d'un atlas probabiliste de perfusion cérébrale en imagerie médicale., septembre 2004