Metadata-Version: 2.1
Name: nitransforms
Version: 20.0.0rc5
Summary: NiTransforms -- Neuroimaging spatial transforms in Python.
Home-page: https://github.com/poldracklab/nitransforms
Author: The NiPy developers
Author-email: nipreps@gmail.com
License: MIT License
Project-URL: Manuscript, https://doi.org/10.31219/osf.io/8aq7b
Project-URL: NiBabel, https://github.com/nipy/nibabel/pull/656
Platform: UNKNOWN
Classifier: Development Status :: 2 - Pre-Alpha
Classifier: Intended Audience :: Science/Research
Classifier: Topic :: Scientific/Engineering :: Image Recognition
Classifier: License :: OSI Approved :: BSD License
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: 3.8
Classifier: Programming Language :: Python :: 3.9
Provides: nitransforms
Requires-Python: >=3.7
Description-Content-Type: text/markdown; charset=UTF-8
Provides-Extra: test
Provides-Extra: tests
Provides-Extra: all
License-File: LICENSE

# NiTransforms
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A development repo for [nipy/nibabel#656](https://github.com/nipy/nibabel/pull/656)

## About
Spatial transforms formalize mappings between coordinates of objects in biomedical images.
Transforms typically are the outcome of image registration methodologies, which estimate
the alignment between two images.
Image registration is a prominent task present in nearly all standard image processing
and analysis pipelines.
The proliferation of software implementations of image registration methodologies has
resulted in a spread of data structures and file formats used to preserve and communicate
transforms.
This segregation of formats precludes the compatibility between tools and endangers the
reproducibility of results.
We propose a software tool capable of converting between formats and resampling images
to apply transforms generated by the most popular neuroimaging packages and libraries
(AFNI, FSL, FreeSurfer, ITK, and SPM).
The proposed software is subject to continuous integration tests to check the
compatibility with each supported tool after every change to the code base.
Compatibility between software tools and imaging formats is a necessary bridge 
to ensure the reproducibility of results and enable the optimization and evaluation
of current image processing and analysis workflows.


