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Non-linear statistical models for shape analysis : application to brain imaging

Abstract : This thesis addresses statistical shape analysis, in the context of medical imaging. In the field of medical imaging, shape analysis is used to describe the morphological variability of various organs and tissues. Our focus in this thesis is on the construction of a generative and discriminative, compact and non-linear model, suitable to the representation of shapes. This model is evaluated in the context of the study of a population of Alzheimer's disease patients and a population of healthy controls. Our principal interest here is using the discriminative model to discover morphological differences that are the most characteristic and discriminate best between a given shape class and forms not belonging in that class. The theoretical innovation of our work lies in two principal points first, we propose a tool to extract discriminative difference in the context of the Support Vector Data description (SVDD) framework ; second, all generated reconstructions are anatomicallycorrect. This latter point is due to the non-linear and compact character of the model, related to the hypothesis that the data (the shapes) lie on a low-dimensional, non-linear manifold. The application of our model on real medical data shows results coherent with well-known findings in related research.
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Submitted on : Monday, February 18, 2013 - 5:57:19 PM
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Giorgos Sfikas. Non-linear statistical models for shape analysis : application to brain imaging. Signal and Image processing. Université de Strasbourg, 2012. English. ⟨NNT : 2012STRAD032⟩. ⟨tel-00789793⟩



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