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Verfasst von:Glodeck, Daniel [VerfasserIn]   i
 Hesser, Jürgen [VerfasserIn]   i
 Zheng, Lei [VerfasserIn]   i
Titel:Potential of metric homotopy between intensity and geometry information for multi-modal 3D registration
Verf.angabe:Daniel Glodeck, Jürgen Hesser, Lei Zheng
E-Jahr:2018
Jahr:10 February 2018
Umfang:10 S.
Fussnoten:Gesehen am 03.05.2019
Titel Quelle:Enthalten in: Zeitschrift für medizinische Physik
Ort Quelle:Amsterdam [u.a.] : Elsevier, 1990
Jahr Quelle:2018
Band/Heft Quelle:28(2018), 4, Seite 325-334
ISSN Quelle:1876-4436
Abstract:This paper focuses on a novel strategy increasing robustness with respect to local optima when using Mutual Information (MI) in multi-modal image registration. This is realized by integrating additional geometry information in the cost function. Hereby, the main innovation is a generalization of multi-metric registration approaches by means of a metric homotopy. Particularly we realize a method which automatically determines the parameters of the metric homotopy. To construct the cost function independent of the choice of the optimizer, the weighting is defined as a function of one of the metrics instead of optimizer steps. In addition, a differentiable cost function is developed. In comparison to the commonly used technique to process an intensity based registration on different resolutions, the proposed method is three times faster with unchanged accuracy. It is also shown that in the presence of large landmark errors the proposed method outperforms an approach in accuracy in which both similarity functionals are applied one after the other. The method is evaluated on 3D multi-modal human brain data sets from the Retrospective Image Registration Evaluation Project (RIRE). The evaluation is performed using the evaluation website of the RIRE project to make the registration results of the proposed method easily comparable to other methods. Therefore, the presented results are also available online on the RIRE project page.
DOI:doi:10.1016/j.zemedi.2018.01.004
URL:Bitte beachten Sie: Dies ist ein Bibliographieeintrag. Ein Volltextzugriff für Mitglieder der Universität besteht hier nur, falls für die entsprechende Zeitschrift/den entsprechenden Sammelband ein Abonnement besteht oder es sich um einen OpenAccess-Titel handelt.

Volltext: https://doi.org/10.1016/j.zemedi.2018.01.004
 Volltext: http://www.sciencedirect.com/science/article/pii/S0939388917301435
 DOI: https://doi.org/10.1016/j.zemedi.2018.01.004
Datenträger:Online-Ressource
Sprache:eng
Sach-SW:Image registration
 Multi-metric
 Multi-modal image processing
 Weighting functions
K10plus-PPN:1664568174
Verknüpfungen:→ Zeitschrift

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