Globally Optimal Displacement Fields Using Local Tensor Metric

In this paper, we propose a novel algorithm for regularizing displacement fields in image registration. The method uses the local structure tensor and gradients of the displacement field to impose a local metric, which is then used optimizing a global cost function. The method allows for linear operators, such as tensors and differential operators modeling the underlying physical anatomy of the human body in medical images. The algorithm is tested using output from the Morphon image registration algorithm on MRI data as well as synthetic test data and the result is compared to the initial displacement field. The results clearly demonstrate the power of the method and the unique features brought forth through the global optimization approach.Dynamic Context Atlases for Image Denoising and Patient Safet