Incorporation of Structural Tensor and Driving Force into Log-Demons for Large-Deformation Image Registration

Ying Wen, Le Zhang, Lianghua He, Mengchu Zhou

Research output: Contribution to journalArticlepeer-review

1 Scopus citations

Abstract

Large-deformation image registration is important in theory and application in computer vision, but is a difficult task for non-rigid registration methods. In this paper, we propose a structural Tensor and Driving force-based Log-Demons algorithm for it, named TDLog-Demons for short. The structural tensor of an image is proposed to obtain a highly accurate deformation field. The driving force is proposed to solve the registration issue of large-deformation that often causes Log-Demons to trap into local minima. It is defined as a point correspondence obtained via multisupport-region-order-based gradient histogram descriptor matching on image's boundary points. It is integrated into an exponentially decreasing form with the velocity field of Log-Demons to move the points accurately and to speed up a registration process. Consequently, the driving force-based Log-Demons can well deal with large-deformation image registration. Extensive experiments demonstrate that the TDLog-Demons not only captures large deformations at a high accuracy but also yields a smooth deformation.

Original languageEnglish (US)
Article number8746814
Pages (from-to)6091-6102
Number of pages12
JournalIEEE Transactions on Image Processing
Volume28
Issue number12
DOIs
StatePublished - Dec 2019

All Science Journal Classification (ASJC) codes

  • Software
  • Computer Graphics and Computer-Aided Design

Keywords

  • Image registration
  • Log-Demons algorithm
  • driving force
  • optimization
  • tensor

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