Vectorial total variation-based regularization for variational image registration.
Where this comes from
- Record sourced from PubMed, PMID 23893729.
- Also identified by DOI 10.1109/TIP.2013.2274749.
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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.
Medical subject headings
- Algorithms
- Artificial Intelligence
- Image Enhancement
- Image Interpretation, Computer-Assisted
- Pattern Recognition, Automated