| Name | pyezzi JSON |
| Version |
0.8.1
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| Summary | None |
| upload_time | 2025-07-16 07:46:40 |
| maintainer | None |
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| author | None |
| requires_python | >=3.10 |
| license | None |
| keywords |
medical
image
processing
|
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======
pYezzi
======
Compute the thickness of a solid using Yezzi and Prince method described in
the article "An Eulerian PDE Approach for Computing Tissue Thickness", IEEE
TRANSACTIONS ON MEDICAL IMAGING, VOL. 22, NO. 10, OCTOBER 2003. [#]_
A C implementation by Rubén Cárdenes [#]_ helped me a lot writing this,
especially the anisotropic part.
.. [#] http://dx.doi.org/10.1109/tmi.2003.817775
.. [#] http://www.dtic.upf.edu/~rcardenes/Ruben_Cardenes/Software.html
Requirements
============
Runtime: numpy.
Build time: cython.
Test time: scikit-image, scipy.
Installation instruction
========================
Available on pypi. [#]_
Use pip: ``pip install pyezzi``
Alternatively, clone the repository and build cython modules with
``pip install .``.
.. [#] https://pypi.python.org/pypi/pyezzi
Usage
=====
Command line
------------
This package provides a basic CLI. Example usage:
.. code:: bash
pyezzi /path/to/endo.mha /path/to/epi.mha /path/to/output.mha [--weights /path/to/thickness_weights.mha]
Python API
----------
Full API documentation is available on
`gitlabpages.inria.fr <https://ncedilni.gitlabpages.inria.fr/pyezzi>`_.
.. code:: python
from pyezzi import compute_thickness_cardiac
thickness = compute_thickness_cardiac(endo, epi)
``endo`` and ``epi`` are numpy binary masks.
``endo`` represents the "inside" boundary of the domain, e.g., the cardiac ventricular blood pool.
``epi`` represents the "outside" boundary of the domain, e.g., the cardiac ventricular epicardium.
A ``spacing`` parameter specifying the spacing between voxels along the axes
can optionnaly be specified.
A ``weights`` parameter can be added to account for "holes" in the wall, cf
"Cedilnik & Peyrat, Weighted tissue thickness, FIMH 2023". [#]_
Check out the included jupyter notebooks in the ``example`` folder for more
details.
.. [#] https://hal.science/hal-04111276/
Contributions
=============
We recommend using `uv <https://docs.astral.sh/uv/>`_ for project management
and `pre-commit <https://pre-commit.com/>`_ to ensure code quality.
After cloning, use ``uv sync --frozen --all-groups --all-extras`` to install dev dependencies.
This will set up a virtualenv in ``.venv`` that you can activate with
``source .venv/bin/activate``. Tests can then be run with ``pytest test``.
To build the cython extension modules in place, use ``python setup.py develop``.
License
=======
This work is licensed under the french CeCILL license. [#]_
You're free to use and modify the code, but please cite the original paper and
me.
.. [#] https://www.cecill.info/licences/Licence_CeCILL_V2.1-en.html
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"description": "======\npYezzi\n======\n\nCompute the thickness of a solid using Yezzi and Prince method described in\nthe article \"An Eulerian PDE Approach for Computing Tissue Thickness\", IEEE\nTRANSACTIONS ON MEDICAL IMAGING, VOL. 22, NO. 10, OCTOBER 2003. [#]_\n\nA C implementation by Rub\u00e9n C\u00e1rdenes [#]_ helped me a lot writing this,\nespecially the anisotropic part.\n\n.. [#] http://dx.doi.org/10.1109/tmi.2003.817775\n.. [#] http://www.dtic.upf.edu/~rcardenes/Ruben_Cardenes/Software.html\n\n\nRequirements\n============\n\nRuntime: numpy.\n\nBuild time: cython.\n\nTest time: scikit-image, scipy.\n\n\nInstallation instruction\n========================\n\nAvailable on pypi. [#]_\nUse pip: ``pip install pyezzi``\n\nAlternatively, clone the repository and build cython modules with\n``pip install .``.\n\n.. [#] https://pypi.python.org/pypi/pyezzi\n\nUsage\n=====\n\nCommand line\n------------\n\nThis package provides a basic CLI. Example usage:\n\n.. code:: bash\n\n pyezzi /path/to/endo.mha /path/to/epi.mha /path/to/output.mha [--weights /path/to/thickness_weights.mha]\n\nPython API\n----------\n\nFull API documentation is available on\n`gitlabpages.inria.fr <https://ncedilni.gitlabpages.inria.fr/pyezzi>`_.\n\n.. code:: python\n\n from pyezzi import compute_thickness_cardiac\n\n thickness = compute_thickness_cardiac(endo, epi)\n\n``endo`` and ``epi`` are numpy binary masks.\n``endo`` represents the \"inside\" boundary of the domain, e.g., the cardiac ventricular blood pool.\n``epi`` represents the \"outside\" boundary of the domain, e.g., the cardiac ventricular epicardium.\n\nA ``spacing`` parameter specifying the spacing between voxels along the axes\ncan optionnaly be specified.\n\nA ``weights`` parameter can be added to account for \"holes\" in the wall, cf\n\"Cedilnik & Peyrat, Weighted tissue thickness, FIMH 2023\". [#]_\n\nCheck out the included jupyter notebooks in the ``example`` folder for more\ndetails.\n\n.. [#] https://hal.science/hal-04111276/\n\nContributions\n=============\n\nWe recommend using `uv <https://docs.astral.sh/uv/>`_ for project management\nand `pre-commit <https://pre-commit.com/>`_ to ensure code quality.\n\nAfter cloning, use ``uv sync --frozen --all-groups --all-extras`` to install dev dependencies.\nThis will set up a virtualenv in ``.venv`` that you can activate with\n``source .venv/bin/activate``. Tests can then be run with ``pytest test``.\n\nTo build the cython extension modules in place, use ``python setup.py develop``.\n\nLicense\n=======\n\nThis work is licensed under the french CeCILL license. [#]_\nYou're free to use and modify the code, but please cite the original paper and\nme.\n\n.. [#] https://www.cecill.info/licences/Licence_CeCILL_V2.1-en.html\n",
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