A novel framework for metric-based image registration

Authors:
Qian Xie;Sebastian Kurtek;Gary E. Christensen;Zhaohua Ding;Eric Klassen;Anuj Srivastava
Affiliations:
Department of Statistics, Florida State University;Department of Statistics, Florida State University;Department of Electrical and Computer Engineering, University of Iowa;Institute of Imaging Science, Vanderbilt University;Department of Mathematics, Florida State University;Department of Statistics, Florida State University
Venue:
WBIR'12 Proceedings of the 5th international conference on Biomedical Image Registration
Year:
2012

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Abstract

The registrations of functions and images is a widely-studied problem that has seen a variety of solutions in the recent years. Most of these solutions are based on objective functions that fail to satisfy two most basic and desired properties in registration: (1) invariance under identical warping: since the registration between two images is unchanged under identical domain warping, the cost function evaluating registrations should also remain unchanged; (2) inverse consistency: the optimal registration of image A to B should be the same as that of image B to A. We present a novel registration approach that uses the L2 norm, between certain vector fields derived from images, as an objective function for registering images. This framework satisfies symmetry and invariance properties. We demonstrate this framework using examples from different types of images and compare performances with some recent methods.