Abstract
In this article, we present the flexible open-source toolbox “VariationalRegistration” for non-parametric variational image registration, realized as a module in the Insight segmentation and registration toolkit. The toolbox is designed to test, evaluate and systematically compare the effects of different building blocks of variational registration approaches, i.e. the distance/similarity measure, the regularization method and the transformation model. In its current state, the framework includes implementations of different similarity measures and regularization methods, as well as displacement-based and diffeomorphic transformation models. The implementation of further components is possible and encouraged. The implemented algorithms were applied to different registration problems and extensively tested using publicly accessible image data bases. This paper presents a quantitative evaluation for inter-patient registration using 3D brain MR images of the LONI image data base. The results demonstrate that the implemented variational registration scheme is competitive with other state-of-the-art approaches for non-rigid image registration.
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Ehrhardt, J., Schmidt-Richberg, A., Werner, R., Handels, H. (2015). Variational Registration. In: Handels, H., Deserno, T., Meinzer, HP., Tolxdorff, T. (eds) Bildverarbeitung für die Medizin 2015. Informatik aktuell. Springer Vieweg, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-46224-9_37
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DOI: https://doi.org/10.1007/978-3-662-46224-9_37
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