Anzeige
Erschienen in:
2010 | OriginalPaper | Buchkapitel
Nonlinear Elasticity Registration and Sobolev Gradients
verfasst von : Tungyou Lin, Ivo Dinov, Arthur Toga, Luminita Vese
Erschienen in: Biomedical Image Registration
Verlag: Springer Berlin Heidelberg
Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.
Wählen Sie Textabschnitte aus um mit Künstlicher Intelligenz passenden Patente zu finden. powered by
Markieren Sie Textabschnitte, um KI-gestützt weitere passende Inhalte zu finden. powered by
We propose Mooney-Rivlin (MR) nonlinear elasticity of hyperelastic materials and numerical algorithms for image registration in the presence of landmarks and large deformation. An auxiliary variable is introduced to remove the nonlinearity in the derivatives of Euler-Lagrange equations. Comparing the MR elasticity model with the Saint Venant-Kirchhoff elasticity model (SVK), the results show that the MR model gives better matching in fewer iterations. To accelerate the slow convergence due to the lack of smoothness of the
L
2
gradient, we construct a Sobolev
H
1
gradient descent method [13] and take advantage of the smoothing quality of the Sobolev operator
$(Id-\triangle)^{-1}$
. The MR model with Sobolev
H
1
gradient descent (SGMR) improves both matching criterion and computational time substantially. We further apply the
L
2
and Sobolev gradient to landmark registration for multi-modal mouse brain data, and observe faster convergence and better landmark matching for the MR model with Sobolev
H
1
gradient descent.