Diffusion tensor model based smoothing

We provide a unified framework for smoothing noisy brain image data along attributes of choice derived from diffusion tensor imaging. The framework is based on a variational segmentation functional approach that outputs smoothed regions within the white matter that are relatively homogeneous with respect to specific diffusion tensor image properties. The smoothed tensor fields and the associated edge fields are recovered in a number of ways, thus illustrating the applicability of the proposed unified framework for smoothing and feature extraction in support of the anatomic identification of white matter fiber systems in the human brain.