Novel Decomposition of Tensor Distance into Shape and Orientation Distances
نویسندگان
چکیده
A novel geometric framework for decomposition of tensor distance into shape and orientation distances is proposed. We show that such shape distance leads to the development of a novel and robust anisotropy measure that reveals strikingly superior white matter profile of DT-MR brain images than fractional anisotropy (FA) and analytically show that it has a higher signal to noise ratio than FA. Using orientation distance, we show how to rotationally interpolate tensors with a scalar linear interpolation.
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