Introduction to the non-rigid image registration evaluation project (NIREP)

Authors:
Gary E. Christensen;Xiujuan Geng;Jon G. Kuhl;Joel Bruss;Thomas J. Grabowski;Imran A. Pirwani;Michael W. Vannier;John S. Allen;Hanna Damasio
Affiliations:
Department of Electrical and Computer Engineering, The University of Iowa;Department of Electrical and Computer Engineering, The University of Iowa;Department of Electrical and Computer Engineering, The University of Iowa;Department of Neurology, The University of Iowa;Department of Neurology, The University of Iowa;Department of Computer Science, The University of Iowa;Department of Radiology, University of Chicago;The Dornsife Cognitive Neuroscience Imaging Center, Univ. of Southern California;The Dornsife Cognitive Neuroscience Imaging Center, Univ. of Southern California
Venue:
WBIR'06 Proceedings of the Third international conference on Biomedical Image Registration
Year:
2006

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Abstract

Non-rigid image registration (NIR) is an essential tool for morphologic comparisons in the presence of intra- and inter-individual anatomic variations. Many NIR methods have been developed, but are especially difficult to evaluate since point-wise inter-image correspondence is usually unknown, i.e., there is no “Gold Standard” to evaluate performance. The Non-rigid Image Registration Evaluation Project (NIREP) has been started to develop, establish, maintain, and endorse a standardized set of relevant benchmarks and metrics for performance evaluation of nonrigid image registration algorithms. This paper describes the basic framework of the project.