Algebraic methods for direct and feature based registration of diffusion tensor images

  • Authors:

  • Alvina Goh;René Vidal

  • Affiliations:

  • Center for Imaging Science, Department of BME, Johns Hopkins University, Baltimore, MD;Center for Imaging Science, Department of BME, Johns Hopkins University, Baltimore, MD

  • Venue:
  • ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part III
  • Year:
  • 2006

Quantified Score

Hi-index 0.00

Visualization

Abstract

We present an algebraic solution to both direct and feature-based registration of diffusion tensor images under various local deformation models. In the direct case, we show how to linearly recover a local 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. In the feature-based case, we show that the tensor reorientation map can be found in closed form by exploiting the spectral properties of the rotation group. Given this map, solving for an affine deformation becomes a linear problem. We test our approach on synthetic, brain and heart diffusion tensor images.