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Proceedings Paper

Evaluation of five non-rigid image registration algorithms using the NIREP framework
Author(s): Ying Wei; Gary E. Christensen; Joo Hyun Song; David Rudrauf; Joel Bruss; Jon G. Kuhl; Thomas J. Grabowski
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Paper Abstract

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.

Paper Details

Date Published: 12 March 2010
PDF: 10 pages
Proc. SPIE 7623, Medical Imaging 2010: Image Processing, 76232L (12 March 2010); doi: 10.1117/12.844616
Show Author Affiliations
Ying Wei, The Univ. of Iowa (United States)
Gary E. Christensen, The Univ. of Iowa (United States)
Joo Hyun Song, The Univ. of Iowa (United States)
David Rudrauf, The Univ. of Iowa (United States)
Joel Bruss, The Univ. of Iowa (United States)
Jon G. Kuhl, The Univ. of Iowa (United States)
Thomas J. Grabowski, Univ. of Washington (United States)


Published in SPIE Proceedings Vol. 7623:
Medical Imaging 2010: Image Processing
Benoit M. Dawant; David R. Haynor, Editor(s)

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