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

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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).
Original languageEnglish
Title of host publicationMedical Imaging 2015: Image Processing
Number of pages7
PublisherSPIE - International Society for Optical Engineering
Publication date2015
DOIs
Publication statusPublished - 2015
EventSPIE Medical Imaging, 2015 - Orlando, FL, United States
Duration: 21 Feb 201526 Feb 2015

Conference

ConferenceSPIE Medical Imaging, 2015
LandUnited States
ByOrlando, FL
Periode21/02/201526/02/2015
SeriesProceedings of S P I E - International Society for Optical Engineering
Volume9413
ISSN0277-786X

ID: 256217837