Metadata-Version: 2.1
Name: lcreg
Version: 1.0.0
Summary: Efficient 3D rigid and affine image registration
Home-page: https://github.com/p-roesch/lcreg
Author: Peter Rösch
Author-email: lcreg@hs-augsburg.de
License: GPLv3
Project-URL: ResearchGate Project, https://www.researchgate.net/project/Efficient-registration-of-large-3D-images-lcreg
Keywords: 3D image registration
Platform: UNKNOWN
Classifier: Development Status :: 5 - Production/Stable
Classifier: License :: OSI Approved :: GNU General Public License v3 or later (GPLv3+)
Classifier: Operating System :: OS Independent
Classifier: Programming Language :: Python :: 3
Classifier: Environment :: Console
Classifier: Intended Audience :: Science/Research
Classifier: Programming Language :: Cython
Classifier: Programming Language :: Python :: 3 :: Only
Requires-Python: >=3, <4
Description-Content-Type: text/markdown
License-File: LICENSE.txt
Requires-Dist: numpy (>=1.21)
Requires-Dist: scipy (>=1.7)
Requires-Dist: bcolz (>=1.2)
Requires-Dist: psutil (>=5.8)
Requires-Dist: py-cpuinfo (>=8.0)

# *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 3D images even if they 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].


# Tutorials, samples and *bcolz* binaries
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). Furthermore, binary installers for the [*bcolz*](https://github.com/Blosc/bcolz) package have been created in order to support the installation of *lcreg* with recent Python versions. These ressources can be downloaded from [here](https://cloud.hs-augsburg.de/index.php/s/iR8BBZM2n6zcxSp).

# Please give feedback
Please send comments, questions and general feedback to the email address of the project which is lcreg@hs-augsburg.de or use the corresponding functionality of the ResearchGate [project page](https://www.researchgate.net/project/Efficient-registration-of-large-3D-images-lcreg).

# 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](http://www.itksnap.org/pmwiki/pmwiki.php), [bcolz](http://bcolz.blosc.org/en/latest), [numpy](https://numpy.org) [scipy](https://scipy.org/scipylib/index.html) and [cython](https://cython.org). 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 GÃ¤rtner, Evi KÃ¶bele,
Stefan KÃ¶nig, Dominik LÃ¼der, Thomas Obermeier and Sigrid Podratzky for acquiring test images and for keeping computers up and running.

# References
[1] T. Netsch,  P. RÃ¶sch,  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,</br>
[DOI: 10.1109/ICCV.2001.937595](https://ieeexplore.ieee.org/document/937595)
</br>
[2] P. RÃ¶sch 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) 118â€“119,</br> [DOI 10.1007/s11548-018-1766-y](https://doi.org/10.1007/s11548-018-1766-y)


