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

Publikation: KonferencebidragPosterForskningfagfællebedømt

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).
OriginalsprogDansk
Publikationsdato2015
Antal sider1
DOI
StatusUdgivet - 2015
BegivenhedSPIE Medical Imaging 2015 - , Danmark
Varighed: 14 maj 2015 → …

Konference

KonferenceSPIE Medical Imaging 2015
LandDanmark
Periode14/05/2015 → …

ID: 154479535