Medical image processing in Python
Project description
MedPy is a library and script collection for medical image processing in Python. It contains basic functionalities for reading, writing and manipulating large images of arbitrary dimensions.
Additionally some image manipulation scripts are installed under the medpy_-prefix which offer various functionalities. See https://github.com/loli/medpy/wiki for a complete list.
One of the central usages is graph-cuts for image segmentation. Medpy implements a voxel based standard and a label based version.
For some simple examples and further instructions, see also the Wiki.
Troubles? Feel free to write me with any questions / comments / suggestions: oskar.maier@googlemail.com
Code
You can find our sources and single-click downloads:
Main repository on Github.
API documentation for all releases and current development tree can be created using Doxygen
Download as a zip file the current trunk.
API Documentation
http://pythonhosted.org/MedPy See especially the sections Packages and Package Functions.
Requirements
MedPy comes with a number of dependencies and optional functionality that can require you to install additional packages.
Dependencies
Recommendations
Optional functionalities
Installing the dependencies
The dependencies will be automatically installed and compiled when installing MedPy. But numpy and scipy both have their own dependencies, that are not always easy to meet. Luckily, most Unix distributions have them in their repositories. E.g. in Ubuntu, you can install them by calling:
sudo apt-get install python-numpy python-scipy
Alternatively, your can find an installation instruction with all dependencies listed here: http://www.scipy.org/scipylib/building/linux.html
Global installation
Note that the global installation makes MedPy available for all users and requires root privileges.
Installation using PIP
Call:
pip install medpy
Installation using easy_install
Call:
easy_install medpy
Installation from source
Download the sources from https://pypi.python.org/pypi/MedPy/, unpack them, enter the directory and run:
python setup.py install
Local installation
The local install will place MedPy in your user site-packages directory and does not require root privileges. You can find our the location of your personal site-packages directory by calling:
python -c 'import site;print site.USER_SITE'
In some cases, the Python configuration does not find packages in the users site-packages directory, in which case you will have to add it to your PYTHONPATH variable. To make this permanent, add the extension to your .bashrc, e.g. using:
echo "export PYTHONPATH=${PYTHONPATH}:$( python -c 'import site;print site.USER_SITE' )" >> ~/.bashrc
More importantly, the script shipped with MedPy won’t be in your PATH and hence can not be used directly. If your user site-packages directory is e.g. /home/<user>/.local/lib/python2.7/site-packages/, the scripts are most likely to be found under /home/<user>/.local/bin/. Add this directory to your PATH using:
echo "export PATH=${PATH}:/home/<user>/.local/bin/" >> ~/.bashrc
(Don’t forget to replace <user> with your own user name.)
Installation using pip
Call:
pip install --user medpy
Installation using easy_install
Call:
easy_install --user medpy
Installation from source
Download the sources from https://pypi.python.org/pypi/MedPy/, unpack them, enter the directory and run:
python setup.py install --user
Development installation
If you care to work on the source directly, you can install MedPy in development mode. Then the sources will remain and any changes made them them be directly available system-wide.
Installation from source
Download the sources from https://pypi.python.org/pypi/MedPy/, unpack them, enter the directory and run:
python setup.py develop
Uninstall
Only pip supports the removal of Python packages. If you have installed MedPy by other means, you will have to remove the package manually. With pip, call simply:
pip uninstall 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.
Installing recommendations
nibabel and pydicom are both available over the Package Index PyPi:
pip install nibabel pydicom
or:
easy_install nibabel pydicom
Installing with GraphCut support
The GraphCut functionalities of MedPy depend on the max-flow/min-cut library by Boykov and Kolmogorov. During installation, MedPy will try to compile it and its python wrappers. If the compilation fails, MedPy will be installed without the GraphCut module. To enable the GraphCut functionality of MedPy, the dependencies of the library must be met before installing MedPy (although it can always be simply re-installed).
Dependencies
Boost.Python
g++
gcc
These dependencies can be found in the repositories of all major distribution. For e.g. Ubuntu, you can simply call:
sudo apt-get install python-boost build-essentials
License
MedPy is distributed under the GNU General Public License, a version of which can be found in the LICENSE.txt file.
Usage examples
See primarily The Wiki for usage examples.
Simple example
Typical usage often looks like this:
#!/usr/bin python from medpy.io import load, save # load input image no.1 data_input1, header_input1 = load(args.input) # load input image no.2 data_input2, header_input2 = load(args.input) # substract to create difference image data_output = data_input1 - data_input2 # save resulting image save(data_output, "/location/output.nii", header_input1, FALSE)
Script examples
Voxel-based graph-cut
To segment an object in an image using voxel-based graph cuts, the first step is to create some marker image depicting foreground and background of the object, where all ones (1) depict foreground and all twos (2) background. This can be done with any image tool. The graphcut can then be executed using:
medpy_graphcut_voxel.py 10.0 original_image.dcm marker_image.dcm result_image.dcm
, where the output is a binary image depicting the object in the original image.
Region-based graph-cut
These version executes the graph-cut on regions/labels rather than single pixel. It performs therefore substantially faster at a low accuracy cost. For this the original image has first to be split into regions using:
medpy_gradient.py original_image.dcm gradient_image.dcm medpy_itk_watershed.py gradient_image.dcm watershed_image.dcm
The cut itself again required foreground and background markers as in the voxel-based example. The cut is then executed using:
medpy_graphcut_label.py gradient_image.dcm watershed_image.dcm marker_image.dcm result_image.dcm
, where the output is a binary image depicting the object in the original image.
Installing ITK wrappers
This is quite some work and absolutely not fail-save. Only try it, if you must (or are bored with too much time at hand).
1. Installing ITK with Python binding on Ubuntu (>= 10.04)
The Ubuntu repositories provide a package which can simply be installed using:
sudo apt-get install python-insighttoolkit3
But this package wraps only a subset of ITKs functionality and therefore does not unleash MedPy s complete power. The recommendation is to follow the second or third approach.
2. Installing ITK with Python binding on Ubuntu (= 12.04)
If you are running Ubuntu 12.04, you can simply contact the author who will provide you with a pre-compiled Ubuntu package.
3. Compiling ITK with Python bindings on POSIX/Unix platforms
All descriptions are for ITK 3.20 but might also be valid for newer versions.
Getting ITK
Got to http://www.itk.org/ITK/resources/software.html , download the InsightToolkit-3.20.1.tar.gz resp. InsightToolkit-3.20.1.zip archive and unpack it to a folder called somthing like IKT3.20.1/src.
Configuring ITK
Compiling ITK requires cmake which can be found for almost all platforms. Create a new directory IKT3.20.1/build and enter it. Then run:
ccmake ../src
and subsequently hit the c key to configure the build. When finished, hit the t key to toggle the advanced mode and activate the following options:
BUILD_SHARED_LIBS ON ITK_USE_REVIEW ON USE_WRAP_ITK ON
, then c onfigure again. Ignore the warning by pressing e. Now set the following options:
WRAP_FFT OFF WRAP_ITK_DIMS 2;3;4 (or more, if you require) WRAP_ITK_JAVA OFF WRAP_ITK_PYTHON ON WRAP_ITK_TCL OFF WRAP_double ON WRAP_float ON WRAP_signed_char ON WRAP_signed_long ON WRAP_signed_short ON WRAP_unsigned_char ON WRAP_unsigned_long ON WRAP_unsigned_short ON WRAP_<datatype> Select yourself which more to activate.
, and c onfigure another time. Finally press g to generate the make-file.
If cmake signals any errors during the configuration process, try to resolve the dependencies from which they originate.
Compiling ITK
Now that the configuration is done, we can compile ITK. Run:
make -j<number-of-your-porcessors>
and wait. This will take some time, depending on your computer up to 2 days are not unlikely.
If an error occurs, try to understand it and eventually re-run the previpous step with some options changed.
Installing ITK
Install ITK and its Python bindings simply by running:
make install (as root)
Addditional step
The ITK Python bindings require a third-party module called PyBuffer which is shipped with ITK but not automatically compiled. Furthermore it holds a small bug. After finishing the previous steps, create a folder called PyBuffer/src somewhere and copy all files and folders from ITK/src/Wrapping/WrapITK/ExternalProjects/PyBuffer/ into it. Now open itkPyBuffer.txx with an text editor and change the line:
int dimensions[ ImageDimension ];
to:
npy_intp dimensions[ ImageDimension ];
(see http://code.google.com/p/wrapitk/issues/detail?id=39 for patch details). Then create a folder PyBuffer/build, enter it and run:
ccmake ../src
After c onfiguring you will see some warnings. Set:
WrapITK_DIR ITK/bin/Wrapping/WrapITK/
In some cases you will also have to set:
PYTHON_NUMARRAY_INCLUDE_DIR /usr/include/numpy
Now c onfigure again and g enerate. To finalize run:
make make install (as root)
Congratulations, you are done compiling and installing ITK with Python wrappers.
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