Using Landmarks as a Deformation Prior for Hybrid Image Registration

Abstract

Hybrid registration schemes are a powerful alternative to fully automatic registration algorithms. Current methods for hybrid registration either include the landmark information as a hard constraint, which is too rigid and leads to difficult optimization problems, or as a soft-constraint, which introduces a difficult to tune parameter for the landmark accuracy. In this paper we model the deformations as a Gaussian process and regard the landmarks as additional information on the admissible deformations. Using Gaussian process regression, we integrate the landmarks directly into the deformation prior. This leads to a new, probabilistic regularization term that penalizes deformations that do not agree with the modeled landmark uncertainty. It thus provides a middle ground between the two aforementioned approaches, without sharing their disadvantages. Our approach works for a large class of different deformation priors and leads to a known optimization problem in a Reproducing Kernel Hilbert Space.