'Institute of Electrical and Electronics Engineers (IEEE)'
Doi
Abstract
Analytical gradient based non-rigid image registration methods,
using intensity based similarity measures (e.g. mutual information),
have proven to be capable of accurately handling
many types of deformations. While their versatility is largely
in part to their high degrees of freedom, the computation of
the gradient of the similarity measure with respect to the many
warp parameters becomes very time consuming. Recently, a
simple stochastic approximation method using a small random
subset of image pixels to approximate this gradient has
been shown to be effective. We propose to use importance
sampling to improve the accuracy and reduce the variance of
this approximation by preferentially selecting pixels near image
edges. Initial empirical results show that a combination of
stochastic approximation methods and importance sampling
greatly improves the rate of convergence of the registration
process while preserving accuracy.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/86019/1/Fessler217.pd
