Boundary-constrained inverse consistent image registration and its applications

This dissertation presents a new inverse consistent image registration (ICIR) method

called boundary-constrained inverse consistent image registration (BICIR).

ICIR algorithms jointly estimate the

forward and reverse transformations between two images while minimizing

the inverse consistency error (ICE).

The ICE at a point is defined as the distance between

the starting and ending location of a point mapped through the forward

transformation and then the reverse transformation.

The novelty of the BICIR method is that a region of interest (ROI) in one

image is registered with its corresponding ROI. This is accomplished

by first registering the boundaries of the ROIs and then matching the

interiors of the ROIs using intensity registration.

The advantages of this approach include providing better registration

at the boundary of the ROI, eliminating registration errors caused by

registering regions outside the ROI, and theoretically

minimizing computation time since only the ROIs are registered.

The first step of the BICIR algorithm is to inverse consistently

register the boundaries of the ROIs. The resulting forward and reverse

boundary transformations are extended to the entire ROI domains

using the Element Free Galerkin Method (EFGM). The transformations

produced by the EFGM are then made inverse consistent by iteratively

minimizing the ICE. These transformations are used as initial conditions

for inverse-consistent intensity-based registration of the ROI interiors.

Weighted extended B-splines (WEB-splines) are used to parameterize the

transformations. WEB-splines are used instead of B-splines since

WEB-splines can be defined over an arbitrarily shaped ROI.

Results are presented showing that the BICIR method provides better

registration of 2D and 3D anatomical images than the small-deformation,

inverse-consistent, linear-elastic (SICLE) image registration algorithm which

registers entire images. Specifically, the BICIR method produced

registration results with lower similarity cost, reduced boundary

matching error, increased ROI relative overlap,

and lower inverse consistency error than the SICLE algorithm.