Thèse soutenue

Analyse quantitative de courbes ouvertes en imagerie cérébrale : application aux faisceaux de matière blanche et aux sillons corticaux

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Auteur / Autrice : Meenakshi Mani
Direction : Christian Barillot
Type : Thèse de doctorat
Discipline(s) : Traitement du signal et télécommunications
Date : Soutenance en 2011
Etablissement(s) : Rennes 1
Ecole(s) doctorale(s) : École doctorale Mathématiques, télécommunications, informatique, signal, systèmes, électronique (Rennes)
Partenaire(s) de recherche : autre partenaire : Université européenne de Bretagne (2007-2016)

Mots clés

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Résumé

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This thesis is a study of how the physical attributes of open curves can be used to advantage in the many varied quantitative applications of white matter fibers and sulci. Shape, scale, orientation and position, the four physical features associated with open curves, have different properties so the usual approach has been to design different metrics and spaces to treat them individually. We use a comprehensive Riemannian framework where joint feature spaces allow for analysis of combinations of features. This is an alternative approach where we can compare curves using geodesic distances. In this thesis, we validate the metrics we use, demonstrate practical uses and apply the tools to important clinical problems. To begin, specific tract configurations in the corpus callosum are used to showcase clustering results that vary with the different Riemannian distance metrics. This nicely argues for the judicious selection of metrics in various applications, a central premise in our work. The framework also provides tools for computing statistical summaries of curves, a first step in statistical analysis. We represent fiber bundles with a mean and variance which describes their essential characteristics. This is a convenient way to work with the large volume in white matter fiber analysis. Next, we design and implement methods to detect morphological changes in the corpus callosum and to track progressive white matter disease. With sulci, we address the specific problem of labeling. An evaluation of physical features and methods such as clustering leads us to a pattern matching solution in which the sulcal configuration itself is the best feature.