Recursive Green’s Function Registration
Non-parametric image registration is still among the most challenging problems in both computer vision and medical imaging. Here, one tries to minimize a joint functional that is comprised of a similarity measure and a regularizer in order to obtain a reasonable displacement field that transforms one image to the other. A common way to solve this problem is to formulate a necessary condition for an optimizer, which in turn leads to a system of partial differential equations (PDEs). In general, the most time consuming part of the registration task is to find a numerical solution for such a system. In this paper, we present a generalized and efficient numerical scheme for solving such PDEs simply by applying 1-dimensional recursive filtering to the right hand side of the system based on the Greens function of the differential operator that corresponds to the chosen regularizer. So in the end we come up with a general linear algorithm. We present the associated Greens function for the diffusive and curvature regularizers and show how one may efficiently implement the whole process by using recursive filter approximation. Finally, we demonstrate the capability of the proposed method on realistic examples. Keywords: Nonparametric
Nonparametric Image Registration Green’s Function Recursive Filter
Bjorn Beuthien Ali Kamen Bernd Fischer
Institute of Mathematics and Image Computing, University of Lubeck, GermanyFraunhofer MEVIS, Project Siemens Corporate Research, Princeton NJ, USA Institute of Mathematics and Image Computing, University of Lubeck, Germany Fraunhofer MEVIS, Projec
国际会议
北京
英文
546-553
2010-09-01（万方平台首次上网日期，不代表论文的发表时间）