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Proceedings Paper

Landmark-driven parameter optimization for non-linear image registration
Author(s): Alexander Schmidt-Richberg; René Werner; Jan Ehrhardt; Jan-Christoph Wolf; Heinz Handels
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Paper Abstract

Image registration is one of the most common research areas in medical image processing. It is required for example for image fusion, motion estimation, patient positioning, or generation of medical atlases. In most intensity-based registration approaches, parameters have to be determined, most commonly a parameter indicating to which extend the transformation is required to be smooth. Its optimal value depends on multiple factors like the application and the occurrence of noise in the images, and may therefore vary from case to case. Moreover, multi-scale approaches are commonly applied on registration problems and demand for further adjustment of the parameters. In this paper, we present a landmark-based approach for automatic parameter optimization in non-linear intensity-based image registration. In a first step, corresponding landmarks are automatically detected in the images to match. The landmark-based target registration error (TRE), which is shown to be a valid metric for quantifying registration accuracy, is then used to optimize the parameter choice during the registration process. The approach is evaluated for the registration of lungs based on 22 thoracic 4D CT data sets. Experiments show that the TRE can be reduced on average by 0.07 mm using automatic parameter optimization.

Paper Details

Date Published: 11 March 2011
PDF: 8 pages
Proc. SPIE 7962, Medical Imaging 2011: Image Processing, 79620T (11 March 2011); doi: 10.1117/12.877059
Show Author Affiliations
Alexander Schmidt-Richberg, Univ. zu Lübeck (Germany)
René Werner, Univ. zu Lübeck (Germany)
Jan Ehrhardt, Univ. zu Lübeck (Germany)
Jan-Christoph Wolf, Univ. zu Lübeck (Germany)
Heinz Handels, Univ. zu Lübeck (Germany)

Published in SPIE Proceedings Vol. 7962:
Medical Imaging 2011: Image Processing
Benoit M. Dawant; David R. Haynor, Editor(s)

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