Metadata-Version: 1.1
Name: pyezzi
Version: 0.2.3
Summary: Thickness calculation on binary 3D images
Home-page: https://gitlab.inria.fr/ncedilni/pyezzi
Author: Nicolas Cedilnik
Author-email: nicoco@nicoco.fr
License: GPL
Description: ======
        pYezzi
        ======
        
        Compute the thickness of a solid using Anthony J. Yezzi's method described in
        the article "An Eulerian PDE Approach for Computing Tissue Thickness, IEEE
        TRANSACTIONS ON MEDICAL IMAGING, VOL. 22, NO. 10, OCTOBER 2003":
        http://dx.doi.org/10.1109/tmi.2003.817775
        
        A C implementation by Rubén Cárdenes can be found at
        http://www.dtic.upf.edu/~rcardenes/Ruben_Cardenes/Software.html and helped me
        write this, especially the anisotropic part.
        
        Requirements
        ============
        
        numpy, cython, scikit-image. Tested with Debian Jessie and Fedora 24-25,
        miniconda-python 3.5.2, cython 0.24, numpy 1.11.2, scikit image 0.12.3
        
        
        Installation instruction
        ========================
        
        Available on pypi: https://pypi.python.org/pypi/pyezzi .
        Use pip: ``pip install pyezzi``
        
        Alternatively, clone the repository and build cython modules with
        ``python setup.py build_ext --inplace``.
        
        Usage
        =====
        
        .. code:: python
        
            from pyezzi.thickness import compute_thickness
            thickness = compute_thickness(labeled_image, debug=True)
        
        ``labeled_image`` is a 3 dimensional numpy array where the wall is labeled 2 and
        the interior is labeled 1.
        
        A ``spacing`` parameter specifying the spacing between voxels along the axes can
        optionnaly be specified.
        
        Check out the included jupyter notebooks in the ``example`` folder for more
        details.
        
        Contributions
        =============
        Feel free to submit pull requests.
        I know the code is nowhere near optimal as it is.
        
        License
        =======
        
        You're free to use and modify the code, but please cite the original paper and
        me.
        
Keywords: medical image processing
Platform: UNKNOWN
Classifier: Development Status :: 4 - Beta
Classifier: Intended Audience :: Developers
Classifier: Intended Audience :: Education
Classifier: Intended Audience :: Science/Research
Classifier: Programming Language :: Python :: 3.4
Classifier: Programming Language :: Python :: 3.5
Classifier: Programming Language :: Python :: 3.6
