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Diffusion Tensor Imaging of the Human Skeletal Muscle : Contributions and Applications

Abstract : In this thesis, we present several techniques for the processing of diffusion tensor images. They span a wide range of tasks such as estimation and regularization, clustering and segmentation, as well as registration. The variational framework proposed for recovering a tensor field from noisy diffusion weighted images exploits the fact that diffusion data represent populations of fibers and therefore each tensor can be reconstructed using a weighted combination of tensors lying in its neighborhood. The segmentation approach operates both at the voxel and the fiber tract levels. It is based on the use of Mercer kernels over Gaussian diffusion probabilities to model tensor similarity and spatial interactions, allowing the definition of fiber metrics that combine information from spatial localization and diffusion tensors. Several clustering techniques can be subsequently used to segment tensor fields and fiber tractographies. Moreover, we show how to develop supervised extensions of these algorithms. The registration algorithm uses probability kernels in order to match moving and target images. The deformation consistency is assessed using the distortion induced in the distances between neighboring probabilities. Discrete optimization is used to seek an optimum of the defined objective function. The experimental validation is done over a dataset of manually segmented diffusion images of the lower leg muscle for healthy and diseased subjects. The results of the techniques developed throughout this thesis are promising.
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Submitted on : Wednesday, July 21, 2010 - 9:51:14 AM
Last modification on : Friday, April 10, 2020 - 2:11:20 AM
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  • HAL Id : tel-00504678, version 1



Radhouène Neji. Diffusion Tensor Imaging of the Human Skeletal Muscle : Contributions and Applications. General Mathematics [math.GM]. Ecole Centrale Paris, 2010. English. ⟨NNT : 2010ECAPO005⟩. ⟨tel-00504678⟩



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