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Efficient 3D rigid and affine image registration

Project description

lcreg - Efficient registration of large 3D images

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 [1] using a novel image encoding scheme fostering on-the-fly image compression and decompression [2].

Tutorial and samples

The lcreg tutorial provides a step by step guide for the installation and practical application of the software and is complemented by sample data and configuration files (156 MB). These ressources can be downloaded from here.

Contact and support

ResearchGate members please use the project page to post comments or ask questions. The email address of the project is lcreg@hs-augsburg.de.

Acknowledgements

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, numpy scipy 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.

References

[1] T. Netsch, P. Rösch, A. v. Muiswinkel and J. Weese: Towards Real-Time Multi-Modality 3-D Medical Image Registration. Eight IEEE International Conference on Computer Vision, ICCV (2001) 718-725,
DOI: 10.1109/ICCV.2001.937595
[2] P. Rösch and K.-H. Kunzelmann: Efficient 3D rigid Registration of Large Micro CT Images. International Journal of Computer assisted Radiology and Surgery 13 (Suppl. 1) (2018) 118–119,
DOI 10.1007/s11548-018-1766-y

Project details


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Files for lcreg, version 0.1.3
Filename, size File type Python version Upload date Hashes
Filename, size lcreg-0.1.3-cp36-cp36m-macosx_10_14_x86_64.whl (174.8 kB) File type Wheel Python version cp36 Upload date Hashes View
Filename, size lcreg-0.1.3-cp36-cp36m-win_amd64.whl (144.4 kB) File type Wheel Python version cp36 Upload date Hashes View
Filename, size lcreg-0.1.3-cp37-cp37m-macosx_10_14_x86_64.whl (174.6 kB) File type Wheel Python version cp37 Upload date Hashes View
Filename, size lcreg-0.1.3-cp37-cp37m-win_amd64.whl (144.5 kB) File type Wheel Python version cp37 Upload date Hashes View
Filename, size lcreg-0.1.3-cp38-cp38-macosx_10_14_x86_64.whl (172.7 kB) File type Wheel Python version cp38 Upload date Hashes View
Filename, size lcreg-0.1.3-cp38-cp38-win_amd64.whl (145.6 kB) File type Wheel Python version cp38 Upload date Hashes View
Filename, size lcreg-0.1.3.tar.gz (230.2 kB) File type Source Python version None Upload date Hashes View

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