Metadata-Version: 1.1
Name: MedPy
Version: 0.2.2
Summary: Medical image processing in Python
Home-page: https://github.com/loli/medpy
Author: Oskar Maier
Author-email: oskar.maier@googlemail.com
License: LICENSE.txt
Description: =========
          MedPy
        =========
        
        **MedPy** is a library and script collection for medical image processing in Python, providing basic functionalities for **reading**, **writing** and **manipulating** large images of **arbitrary dimensionality**.
        Its main contributions are n-dimensional versions of popular **image filters**, a collection of **image feature extractors**, ready to be used with `scikit-learn <http://scikit-learn.org/>`_, and an exhaustive n-dimensional **graph-cut** package.
        
        **Troubles?** Feel free to write me with any questions / comments / suggestions: oskar.maier@googlemail.com
        
        **Found a bug?** https://github.com/loli/medpy/issues
        
        
        Installing MedPy the fast way (Ubuntu and derivatives)
        ======================================================
        First::
        
            sudo apt-get install python-pip python-numpy python-scipy libboost-python-dev build-essential
            
        Then::
        
            sudo pip install nibabel pydicom medpy
         
        Done. More installation instructions can be found in the `documentation <http://pythonhosted.org/MedPy/>`_.
        
        
        Getting started with the library
        ================================
        If you already have one, whose format is support (see in the `documentation <http://pythonhosted.org/MedPy/>`_.), then good.
        Otherwise navigate to http://www.nitrc.org/projects/inia19, click on the *Download Now* button, unpack and look for the *inia19-t1.nii* file.
        Open it in your favorite medical image viewer (I personally fancy `itksnap <http://www.itksnap.org>`_) and beware a the INIA19 primate brain atlas.
        
        Load the image
        
        >>> from medpy.io import load
        >>> image_data, image_header = load('/path/to/image.xxx')
        
        The data is stored in a numpy ndarray, the header is an object containing additional metadata, such as the voxel-spacing.
        No lets take a look at some of the image metadata
        
        >>> image_data.shape
        (168, 206, 128)
        >>> image_data.dtype
        dtype(float32)
        
        And the header gives us
        
        >>> from medpy.io import header
        >>> header.get_pixel_spacing(image_header)
        (0.5, 0.5, 0.5)
        >>> header.get_offset(image_header)
        (0.0, 0.0, 0.0)
        
        Now lets apply one of the **MedPy** filter, more exactly the Otsu thresholding, which can be used for automatic background removal
        
        >>> from medpy.filter import otsu
        >>> threshold = otsu(image_data)
        >>> output_data = image_data > threshold
        
        And save the binary image, marking the foreground
        
        >>> from medpy.io import save
        >>> save(output_data, '/path/to/otsu.xxx', image_header)
        
        After taking a look at it, you might want to dive deeper with the `documentation <http://pythonhosted.org/MedPy/>`_.
        
        
        Getting started with the library
        ================================
        Get an image as described above. Now::
        
        	medpy_info.py /path/to/image.xxx
        	
        will give you some details about the image. With::
        
        	medpy_diff.py /path/to/image1.xxx /path/to/image2.xxx
        
        you can compare two image. And::
        
        	medpy_anisotropic_diffusion.py /path/to/image.xxx /path/to/output.xxx
        	
        lets you apply an edge preserving anisotropic diffusion filter. For a list of all scripts, see the `documentation <http://pythonhosted.org/MedPy/>`_.
        
        
        Read/write support for medical image formats
        ============================================
        MedPy builds on 3rd party modules to load and save images. Currently
        implemented are the usages of
        
        * NiBabel
        * PyDicom
        * ITK
        
        , each of which supports the following formats.
        
        **NiBabel** enables support for:
        
        * NifTi - Neuroimaging Informatics Technology Initiative (.nii, nii.gz)
        * Analyze (plain, SPM99, SPM2) (.hdr/.img, .img.gz)
        * and some others more (http://nipy.sourceforge.net/nibabel/)
        
        **PyDicom** enables support for:
        
        * Dicom - Digital Imaging and Communications in Medicine (.dcm, .dicom)
        
        **ITK** enables support for:
        
        * NifTi - Neuroimaging Informatics Technology Initiative (.nii, nii.gz)
        * Analyze (plain, SPM99, SPM2) (.hdr/.img, .img.gz)
        * Dicom - Digital Imaging and Communications in Medicine (.dcm, .dicom)
        * Itk/Vtk MetaImage (.mhd, .mha/.raw)
        * Nrrd - Nearly Raw Raster Data (.nhdr, .nrrd)
        * and many others more (http://www.cmake.org/Wiki/ITK/File_Formats)
        
        For some functionalities, which are collected in the *medpy.itkvtk* package **ITK** is also required.
        
        
        Code
        ====
        You can find our sources and single-click downloads:
        
        * `Main repository <https://github.com/loli/medpy>`_ on Github.
        * API documentation for all releases and current development tree can be created using `Doxygen <http://www.doxygen.org>`_
        * Download as a zip file the `current trunk <https://github.com/loli/medpy/archive/master.zip>`_.
        
        
        Tutorials and API Documentation
        ===============================
        http://pythonhosted.org/MedPy 
        
        Requirements
        ============
        MedPy comes with a number of dependencies and optional functionality that can require you to install additional packages.
        
        Dependencies
        ------------
        
        * `scipy <http://www.scipy.org/>`_ >= 0.9.0
        * `numpy <http://www.numpy.org/>`_ >= 1.6.1
        
        Recommendations
        ---------------
        * `nibabel <http://nipy.sourceforge.net/nibabel//>`_ >= 1.3.0 (enables support for NIfTI and Analyze image formats)
        * `pydicom <http://code.google.com/p/pydicom/>`_ >= 0.9.7 (enables support for DICOM image format)
        
        Optional functionalities
        ------------------------
        * compilation with `max-flow/min-cut` (enables the GraphCut functionalities)
        * `itk <http://www.itk.org/>`_ >= 3.16.0 with `WrapITK <http://code.google.com/p/wrapitk/>`_ (enables support for a large number of image formats)
        
        
        License
        =======
        MedPy is distributed under the GNU General Public License, a version of which can be found in the LICENSE.txt file.
        
        
Keywords: medical image processing dicom itk insight tool kit MRI CT US graph cut max-flow min-cut
Platform: UNKNOWN
Classifier: Development Status :: 4 - Beta
Classifier: Environment :: Console
Classifier: Environment :: Other Environment
Classifier: Intended Audience :: End Users/Desktop
Classifier: Intended Audience :: Developers
Classifier: Intended Audience :: Healthcare Industry
Classifier: Intended Audience :: Science/Research
Classifier: License :: OSI Approved :: GNU General Public License (GPL)
Classifier: Operating System :: POSIX
Classifier: Operating System :: Unix
Classifier: Programming Language :: Python
Classifier: Programming Language :: C++
Classifier: Topic :: Scientific/Engineering :: Medical Science Apps.
Classifier: Topic :: Scientific/Engineering :: Image Recognition
