Date of Degree
PhD (Doctor of Philosophy)
Electrical and Computer Engineering
Gary Edward Christensen
First Committee Member
Joseph M Reinhardt
Second Committee Member
Hans J Johnson
Third Committee Member
Fourth Committee Member
Mona K Garvin
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.
Image Registration, Inverse Consistent, Medical Imaging, Splines, Surface Registration, WEB-splines
ix, 132 pages
Includes bibliographical references (pages 127-132).
Copyright 2011 Dinesh Kumar