An uncertainty-driven hybrid of intensity-based and feature-based registration with application to retinal and lung CT images

Abstract

A new hybrid of feature-based and intensity-based registration is presented. The algorithm reflects a new understanding of the role of alignment error in the generation of registration constraints. This leads to an iterative process where distinctive image locations from the moving image are matched against the intensity structure of the fixed image. The search range of this matching process is controlled by both the uncertainty in the current transformation estimate and the properties of the image locations to be matched. The resulting hybrid algorithm is applied to retinal image registration by incorporating it as the main estimation engine within our recently published Dual-Bootstrap ICP algorithm. The hybrid algorithm is used to align serial and 4d CT images of the lung using a B-spline based deformation model. © Springer-Verlag Berlin Heidelberg 2004.