@Article{stewart:tmi03,
author = {C.\ V.\ Stewart and Chia-Ling Tsai and B.\ Roysam},
title = {The dual-bootstrap iterative closest point algorithm
with application to retinal image registration},
journal = {Medical Imaging, IEEE Transactions on},
year = 2003,
volume = 22,
number = 11,
pages = {1379--1394},
keywords = {blood vessels, covariance matrices, eye, image
matching, image registration, iterative methods,
medical image processing, blood vessel centerlines,
covariance matrix, dual-bootstrap iterative closest
point algorithm, estimation refinement, image
alignment, individual vascular landmarks, quadratic
transformations, retinal image registration},
abstract = {Motivated by the problem of retinal image
registration, this paper introduces and analyzes a
new registration algorithm called Dual-Bootstrap
Iterative Closest Point (Dual-Bootstrap ICP). The
approach is to start from one or more initial,
low-order estimates that are only accurate in small
image regions, called bootstrap regions. In each
bootstrap region, the algorithm iteratively: 1)
refines the transformation estimate using
constraints only from within the bootstrap region;
2) expands the bootstrap region; and 3) tests to see
if a higher order transformation model can be used,
stopping when the region expands to cover the
overlap between images. Steps 1): and 3), the
bootstrap steps, are governed by the covariance
matrix of the estimated transformation. Estimation
refinement [Step 2)] uses a novel robust version of
the ICP algorithm. In registering retinal image
pairs, Dual-Bootstrap ICP is initialized by
automatically matching individual vascular
landmarks, and it aligns images based on detected
blood vessel centerlines. The resulting quadratic
transformations are accurate to less than a
pixel. On tests involving approximately 6000 image
pairs, it successfully registered 99.5\% of the
pairs containing at least one common landmark, and
100\% of the pairs containing at least one common
landmark and at least 35\% image overlap.},
issn = {0278-0062},
annote = {}
}