Image registration using stationary velocity fields parameterized by norm-minimizing Wendland kernel
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Image registration using stationary velocity fields parameterized by norm-minimizing Wendland kernel. / Pai, Akshay Sadananda Uppinakudru; Sommer, Stefan Horst; Sørensen, Lauge; Darkner, Sune; Sporring, Jon; Nielsen, Mads.
Medical Imaging 2015: Image Processing. SPIE - International Society for Optical Engineering, 2015. (Proceedings of S P I E - International Society for Optical Engineering, Vol. 9413).Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review
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TY - GEN
T1 - Image registration using stationary velocity fields parameterized by norm-minimizing Wendland kernel
AU - Pai, Akshay Sadananda Uppinakudru
AU - Sommer, Stefan Horst
AU - Sørensen, Lauge
AU - Darkner, Sune
AU - Sporring, Jon
AU - Nielsen, Mads
PY - 2015
Y1 - 2015
N2 - Interpolating kernels are crucial to solving a stationary velocity field (SVF) based image registration problem. This is because, velocity fields need to be computed in non-integer locations during integration. The regularity in the solution to the SVF registration problem is controlled by the regularization term. In a variational formulation, this term is traditionally expressed as a squared norm which is a scalar inner product of the interpolating kernels parameterizing the velocity fields. The minimization of this term using the standard spline interpolation kernels (linear or cubic) is only approximative because of the lack of a compatible norm. In this paper, we propose to replace such interpolants with a norm-minimizing interpolant - the Wendland kernel which has the same computational simplicity like B-Splines. An application on the Alzheimer's disease neuroimaging initiative showed that Wendland SVF based measures separate (Alzheimer's disease v/s normal controls) better than both B-Spline SVFs (p<0.05 in amygdala) and B-Spline freeform deformation (p<0.05 in amygdala and cortical gray matter).
AB - Interpolating kernels are crucial to solving a stationary velocity field (SVF) based image registration problem. This is because, velocity fields need to be computed in non-integer locations during integration. The regularity in the solution to the SVF registration problem is controlled by the regularization term. In a variational formulation, this term is traditionally expressed as a squared norm which is a scalar inner product of the interpolating kernels parameterizing the velocity fields. The minimization of this term using the standard spline interpolation kernels (linear or cubic) is only approximative because of the lack of a compatible norm. In this paper, we propose to replace such interpolants with a norm-minimizing interpolant - the Wendland kernel which has the same computational simplicity like B-Splines. An application on the Alzheimer's disease neuroimaging initiative showed that Wendland SVF based measures separate (Alzheimer's disease v/s normal controls) better than both B-Spline SVFs (p<0.05 in amygdala) and B-Spline freeform deformation (p<0.05 in amygdala and cortical gray matter).
U2 - 10.1117/12.2076469
DO - 10.1117/12.2076469
M3 - Article in proceedings
T3 - Proceedings of S P I E - International Society for Optical Engineering
BT - Medical Imaging 2015: Image Processing
PB - SPIE - International Society for Optical Engineering
Y2 - 21 February 2015 through 26 February 2015
ER -
ID: 256217837