ITKElastix
==========
[](https://pypi.python.org/pypi/itk-elastix)
[](https://mybinder.org/v2/gh/InsightSoftwareConsortium/ITKElastix/master?urlpath=lab/tree/examples%2FITK_Example1_SimpleRegistration.ipynb)
[](https://github.com/InsightSoftwareConsortium/ITKElastix/blob/master/LICENSE)
[](https://zenodo.org/badge/latestdoi/207451937)
Overview
--------
Provides an [ITK](https://www.itk.org) Python interface to [elastix](http://elastix.isi.uu.nl/), a toolbox for rigid and nonrigid registration of images.
elastix is open source software, based on the well-known [Insight Toolkit (ITK)](https://discourse.itk.org). The software consists of a collection of algorithms that are commonly used to solve (medical) image registration problems. The modular design of elastix allows the user to quickly configure, test, and compare different registration methods for a specific application.
Installation
------------
Install cross-platform binary Python packages with [pip](https://pypi.org/project/pip/):
pip install itk-elastix
*Experimental* GPU-accelerated packages can be installed on Linux with:
pip install itk-elastix-opencl
Usage
-----
To register two images, traditionally called the fixed image and the moving image:
import itk
fixed_image = itk.imread('path/to/fixed_image.mha')
moving_image = itk.imread('path/to/moving_image.mha')
registered_image, params = itk.elastix_registration_method(fixed_image, moving_image)
Interactive examples and tutorial material can be found in the [examples](https://github.com/InsightSoftwareConsortium/ITKElastix/tree/master/examples) directory. Run the examples in free cloud compute containers [on MyBinder](https://mybinder.org/v2/gh/InsightSoftwareConsortium/ITKElastix/master?urlpath=lab/tree/examples%2FITK_Example1_SimpleRegistration.ipynb) or clone the repository and run the notebooks locally in [Jupyter Notebook or Jupyter Lab](https://jupyter.org/). Try out the *experimental* GPU packages [on Paperspace Gradient](https://www.paperspace.com/temmx3m64/notebook/prdfn7bsz).
ITKElastix can be used with both the [procedural](https://docs.python.org/3/howto/functional.html) and the [object oriented method](https://docs.python.org/3/howto/functional.html), as shown in the example notebooks. The procedural method is shorter, less explicit and currently slightly less functional than the object oriented method, however the execution time and output do not differ apart from possible differences due to the stochastic nature of the Elastix algorithm.
Acknowledgements
----------------
The lead developers of elastix are [Stefan Klein](https://github.com/stefanklein) and [Marius Staring](https://github.com/mstaring).
This software was initially developed at the Image Sciences Institute, under supervision of Josien P.W. Pluim. Today, [many](https://github.com/SuperElastix/elastix/graphs/contributors) have
contributed to elastix.
If you use this software anywhere we would appreciate if you cite the following articles:
- S. Klein, M. Staring, K. Murphy, M.A. Viergever, J.P.W. Pluim,
\"elastix: a toolbox for intensity based medical image
registration,\" IEEE Transactions on Medical Imaging, vol. 29,
no. 1, pp. 196 - 205, January 2010.
- D.P. Shamonin, E.E. Bron, B.P.F. Lelieveldt, M. Smits, S. Klein
and M. Staring, \"Fast Parallel Image Registration on CPU and GPU
for Diagnostic Classification of Alzheimer's Disease\", Frontiers in
Neuroinformatics, vol. 7, no. 50, pp. 1-15, January 2014.
This ITK module is based on [SimpleElastix](http://simpleelastix.github.io/), created by [Kasper Marstal](https://github.com/kaspermarstal). For more information, see:
- Kasper Marstal, Floris Berendsen, Marius Staring and Stefan Klein,
\"SimpleElastix: A user-friendly, multi-lingual library for medical
image registration\", International Workshop on Biomedical Image
Registration (WBIR), Las Vegas, Nevada, USA, 2016