Fast GL(n)-Invariant framework for tensors regularization

Yaniv Gur*, Ofer Pasternak, Nir Sochen

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

13 Scopus citations

Abstract

We propose a novel framework for regularization of symmetric positive-definite (SPD) tensors (e.g., diffusion tensors). This framework is based on a local differential geometric approach. The manifold of symmetric positive-definite (SPD) matrices, P n, is parameterized via the Iwasawa coordinate system. In this framework distances on P n are measured in terms of a natural GL(n)-invariant metric. Via the mathematical concept of fiber bundles, we describe the tensor-valued image as a section where the metric over the section is induced by the metric over P n . Then, a functional over the sections accompanied by a suitable data fitting term is defined. The variation of this functional with respect to the Iwasawa coordinates leads to a set of 1/2n(n+1) coupled equations of motion. By means of the gradient descent method, these equations of motion define a Beltrami flow over P n . It turns out that the local coordinate approach via the Iwasawa coordinate system results in very simple numerics that leads to fast convergence of the algorithm. Regularization results as well as results of fibers tractography for DTI are presented.

Original languageEnglish
Pages (from-to)211-222
Number of pages12
JournalInternational Journal of Computer Vision
Volume85
Issue number3
DOIs
StatePublished - Dec 2009

Keywords

  • Beltrami framework
  • Differential geometry
  • Diffusion tensor imaging
  • Pdes
  • Tensor-valued image

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