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Medical image processing in Python

Project description

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, 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

Too many depoendencies? Try our docker images https://registry.hub.docker.com/u/loli/medpy/ (release) and https://registry.hub.docker.com/u/loli/medpy-autobuilds/ (development)

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.

Using Python 3?

Try:

sudo pip install nibabel pydicom
sudo pip install https://github.com/user/repository/archive/Release_0.3.0p3.zip

Getting started with the library

If you already have one, whose format is support (see in the documentation.), 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) 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.

Getting started with the scripts

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.

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:

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 >= 0.9.0

  • numpy >= 1.6.1

  • nibabel >= 1.3.0 (enables support for NIfTI and Analyze image formats)

  • pydicom >= 0.9.7 (enables support for DICOM image format)

Optional functionalities

  • compilation with max-flow/min-cut (enables the GraphCut functionalities)

  • itk >= 3.16.0 with 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.

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