ECM versus ICP for point registration

Xie, Weiguo; Nolte, Lutz-Peter; Zheng, Guoyan (2011). ECM versus ICP for point registration. In: The 33rd Annual International Conference of the IEEE EMBS (EMBC 2011). IEEE Engineering in Medicine and Biology Society conference proceedings: Vol. 2011 (pp. 2131-2135). Vancouver, Canada: IEEE 10.1109/IEMBS.2011.6090398

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Iterative Closest Point (ICP) is a widely exploited method for point registration that is based on binary point-to-point assignments, whereas the Expectation Conditional Maximization (ECM) algorithm tries to solve the problem of point registration within the framework of maximum likelihood with point-to-cluster matching. In this paper, by fulfilling the implementation of both algorithms as well as conducting experiments in a scenario where dozens of model points must be registered with thousands of observation points on a pelvis model, we investigated and compared the performance (e.g. accuracy and robustness) of both ICP and ECM for point registration in cases without noise and with Gaussian white noise. The experiment results reveal that the ECM method is much less sensitive to initialization and is able to achieve more consistent estimations of the transformation parameters than the ICP algorithm, since the latter easily sinks into local minima and leads to quite different registration results with respect to different initializations. Both algorithms can reach the high registration accuracy at the same level, however, the ICP method usually requires an appropriate initialization to converge globally. In the presence of Gaussian white noise, it is observed in experiments that ECM is less efficient but more robust than ICP.

Item Type:

Conference or Workshop Item (Paper)

Division/Institute:

04 Faculty of Medicine > Pre-clinic Human Medicine > Institute for Surgical Technology & Biomechanics ISTB [discontinued]

UniBE Contributor:

Xie, Weiguo, Nolte, Lutz-Peter, Zheng, Guoyan

Subjects:

500 Science > 570 Life sciences; biology
600 Technology > 610 Medicine & health

ISSN:

1557-170X

ISBN:

978-1-4244-4122-8

Series:

IEEE Engineering in Medicine and Biology Society conference proceedings

Publisher:

IEEE

Language:

English

Submitter:

Factscience Import

Date Deposited:

04 Oct 2013 14:16

Last Modified:

05 Dec 2022 14:04

Publisher DOI:

10.1109/IEMBS.2011.6090398

PubMed ID:

22254759

Web of Science ID:

000298810001314

BORIS DOI:

10.7892/boris.4675

URI:

https://boris.unibe.ch/id/eprint/4675 (FactScience: 209242)

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