• DocumentCode
    57423
  • Title

    Vectorial Total Variation-Based Regularization for Variational Image Registration

  • Author

    Chumchob, N.

  • Author_Institution
    Dept. of Math., Silpakorn Univ., Nakorn Pathom, Thailand
  • Volume
    22
  • Issue
    11
  • fYear
    2013
  • fDate
    Nov. 2013
  • Firstpage
    4551
  • Lastpage
    4559
  • Abstract
    To use interdependence between the primary components of the deformation field for smooth and non-smooth registration problems, the channel-by-channel total variation- or standard vectorial total variation (SVTV)-based regularization has been extended to a more flexible and efficient technique, allowing high quality regularization procedures. Based on this method, this paper proposes a fast nonlinear multigrid (NMG) method for solving the underlying Euler-Lagrange system of two coupled second-order nonlinear partial differential equations. Numerical experiments using both synthetic and realistic images not only confirm that the recommended VTV-based regularization yields better registration qualities for a wide range of applications than those of the SVTV-based regularization, but also that the proposed NMG method is fast, accurate, and reliable in delivering visually-pleasing registration results.
  • Keywords
    image registration; nonlinear differential equations; partial differential equations; Euler-Lagrange system; SVTV-based regularization; fast nonlinear multigrid method; nonsmooth registration problems; primary components; second-order nonlinear partial differential equations; smooth registration problems; standard vectorial total variation-based regularization; variational image registration; Approximation methods; Image registration; Imaging; Materials; Minimization; Standards; Deformable image registration; inverse problems; nonlinear multigrid; regularization; variational image registration; vectorial total variation; Algorithms; Artificial Intelligence; Image Enhancement; Image Interpretation, Computer-Assisted; Pattern Recognition, Automated; Reproducibility of Results; Sensitivity and Specificity;
  • fLanguage
    English
  • Journal_Title
    Image Processing, IEEE Transactions on
  • Publisher
    ieee
  • ISSN
    1057-7149
  • Type

    jour

  • DOI
    10.1109/TIP.2013.2274749
  • Filename
    6567962