Title: 
Example-Based Image Registration via Boosted Classifiers

Authors: 
William Mullally, Stan Sclaroff, and Margrit Betke

Date: March 18, 2009

Abstract:

We propose a novel image registration framework which uses classifiers
trained from examples of aligned images to achieve registration. Our
approach is designed to register images of medical data where the
physical condition of the patient has changed significantly and image
intensities are drastically different. We use two boosted classifiers
for each degree of freedom of image transformation. These two
classifiers can both identify when two images are correctly aligned
and provide an efficient means of moving towards correct registration
for misaligned images. The classifiers capture local alignment
information using multi-pixel comparisons and can therefore achieve
correct alignments where approaches like correlation and
mutual-information which rely on only pixel-to-pixel comparisons
fail. We test our approach using images from CT scans acquired in a
study of acute respiratory distress syndrome. We show significant
increase in registration accuracy in comparison to an approach using
mutual information.