Evaluating non-rigid image registration algorithm performance is a difficult problem since there is rarely a "gold
standard" (i.e., known) correspondence between two images. This paper reports the analysis and comparison
of five non-rigid image registration algorithms using the Non-Rigid Image Registration Evaluation Project
(NIREP) (www.nirep.org) framework. The NIREP framework evaluates registration performance using centralized
databases of well-characterized images and standard evaluation statistics (methods) which are implemented
in a software package. The performance of five non-rigid registration algorithms (Affine, AIR, Demons, SLE and
SICLE) was evaluated using 22 images from two NIREP neuroanatomical evaluation databases. Six evaluation
statistics (relative overlap, intensity variance, normalized ROI overlap, alignment of calcarine sulci, inverse consistency
error and transitivity error) were used to evaluate and compare image registration performance. The
results indicate that the Demons registration algorithm produced the best registration results with respect to the
relative overlap statistic but produced nearly the worst registration results with respect to the inverse consistency
statistic. The fact that one registration algorithm produced the best result for one criterion and nearly the worst
for another illustrates the need to use multiple evaluation statistics to fully assess performance.
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