Local non-rigid image registration using mutual information

Abstract

Recently there has emerged a need to compute multimodal
non-rigid registrations in a lot of clinical applications.
To date, the viscous fluid algorithm is perhaps
the most adept method at recovering large local misregistrations
that exist between two images. However,
this model can only be used on images from the same
modality as it assumes similar intensity values between
images. This paper presents a solution to this problem
by proposing a hybrid non-rigid registration using
the viscous fluid algorithm and mutual information (MI).
The MI is incorporated via the use of a block matching
procedure to generate a sparse deformation field which
drives the viscous fluid algorithm. This algorithmis compared
to two other popular local registration approaches,
namely Gaussian convolution and the thin-plate spline
warp. Results showthat the thin-plate splinewarp and the
MI-Fluid approach produce comparable results. However,
Gaussian convolution is the superior choice, especially
in controlled environments.