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We address the problem of segmenting high angular resolution diffusion images of the brain into cerebral regions corresponding to distinct white matter fiber bundles. We cast this problem as a manifold clustering problem in which distinct fiber bundles correspond to different submanifolds of the space of orientation distribution functions (ODFs). Our approach integrates tools from sparse representation...
The inclusion of the free-running Purkinje network in computational simulations provides a significant insight into understanding the mechanisms of cardiac pathophysiologies. However, its automatic extraction is challenging due to the presence of abundant local complexities. We thereby introduce a novel algorithm to track the Purkinje fibers in high resolution magnetic resonance (MR) images. Our formulation...
We propose a method for epicardial segmentation in dynamic MR sequences using priors on shape, intensity, and dynamics. The prior models are built from a training set of manually segmented sequences. The dynamics of the heart and background intensities are modeled with linear autoregressive models whose parameters are learnt from training data and then used as priors for segmentation purposes. The...
Given a volume of cardiac MR images, we consider the problem of segmenting the heart based on intensity and dynamics. We first segment the heart and the chest from the background using an algebraic technique for intensity based segmentation called polysegment. As the heart and the chest exhibit different dynamics, we model the image temporal evolution as the output of two different linear dynamical...
We present an algebraic solution to direct registration of diffusion tensor images under various local deformation models. We show how to linearly recover the deformation from the partial derivatives of the tensor using the so-called diffusion tensor constancy constraint, a generalization of the brightness constancy constraint to diffusion tensor data. Given the tensor reorientation map, we show that...
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