Image registration using stationary velocity fields parameterized by norm-minimizing Wendland kernel

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Standard

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, Bind 9413).

Publikation: Bidrag til bog/antologi/rapport › Konferencebidrag i proceedings › Forskning › fagfællebedømt

Harvard

Pai, ASU, Sommer, SH, Sørensen, L, Darkner, S, Sporring, J & Nielsen, M 2015, Image registration using stationary velocity fields parameterized by norm-minimizing Wendland kernel. i Medical Imaging 2015: Image Processing. SPIE - International Society for Optical Engineering, Proceedings of S P I E - International Society for Optical Engineering, bind 9413, SPIE Medical Imaging, 2015, Orlando, FL, USA, 21/02/2015. https://doi.org/10.1117/12.2076469

APA

Pai, A. S. U., Sommer, S. H., Sørensen, L., Darkner, S., Sporring, J., & Nielsen, M. (2015). Image registration using stationary velocity fields parameterized by norm-minimizing Wendland kernel. I Medical Imaging 2015: Image Processing SPIE - International Society for Optical Engineering. Proceedings of S P I E - International Society for Optical Engineering Bind 9413 https://doi.org/10.1117/12.2076469

Vancouver

Pai ASU, Sommer SH, Sørensen L, Darkner S, Sporring J, Nielsen M. Image registration using stationary velocity fields parameterized by norm-minimizing Wendland kernel. I Medical Imaging 2015: Image Processing. SPIE - International Society for Optical Engineering. 2015. (Proceedings of S P I E - International Society for Optical Engineering, Bind 9413). https://doi.org/10.1117/12.2076469

Author

Pai, Akshay Sadananda Uppinakudru ; Sommer, Stefan Horst ; Sørensen, Lauge ; Darkner, Sune ; Sporring, Jon ; Nielsen, Mads. / Image registration using stationary velocity fields parameterized by norm-minimizing Wendland kernel. Medical Imaging 2015: Image Processing. SPIE - International Society for Optical Engineering, 2015. (Proceedings of S P I E - International Society for Optical Engineering, Bind 9413).

Bibtex

@inproceedings{f8a19705b6864cad90dbaba7f0ab3ecd,
title = "Image registration using stationary velocity fields parameterized by norm-minimizing Wendland kernel",
abstract = " 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).",
author = "Pai, {Akshay Sadananda Uppinakudru} and Sommer, {Stefan Horst} and Lauge S{\o}rensen and Sune Darkner and Jon Sporring and Mads Nielsen",
year = "2015",
doi = "10.1117/12.2076469",
language = "English",
series = "Proceedings of S P I E - International Society for Optical Engineering",
publisher = "SPIE - International Society for Optical Engineering",
booktitle = "Medical Imaging 2015: Image Processing",
note = "null ; Conference date: 21-02-2015 Through 26-02-2015",

}

RIS

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