Rigid and affine registration of large scalar 3D images is an import step for both medical and non-medical image processing. The distinguishing feature of lcreg is its capability to efficiently register images that do not fit into system memory. lcreg is based on the optimisation of the local correlation similarity measure  using a novel image encoding scheme fostering on-the-fly image compression and decompression .
Many thanks to Karl-Heinz Kunzelmann for his support, many helpful
discussions and for making dental test images available.
This work benefited from the use of ITK-SNAP, bcolz, numpyscipy and cython. The University of Applied Sciences, Augsburg, in particular the Faculty of
Computer Science supported this project by granting sabbatical leaves.
Special thanks to Gisela Dachs, Andreas Gärtner, Evi Köbele,
Stefan König, Dominik Lüder, Thomas Obermeier and Sigrid Podratzky for acquiring test images and for keeping computers up and running.
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