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MedPy

GitHub | Documentation | Tutorials | Issue tracker

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.

Installation

sudo apt-get install libboost-python-dev build-essential
pip3 install medpy

MedPy requires Python 3 and officially supports Ubuntu as well as other Debian derivatives. For installation instructions on other operating systems see the documentation. While the library itself is written purely in Python, the graph-cut extension comes in C++ and has it's own requirements.

Getting started with the library

If you already have a medical image at hand in one of the supported formats, you can use it for this introduction. If not, 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: 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. Now 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

image_header.get_voxel_spacing()

(0.5, 0.5, 0.5)

image_header.get_offset()

(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 tutorials found in the documentation.

Getting started with the scripts

MedPy comes with a range of read-to-use commandline scripts, which are all prefixed by medpy_. To try these examples, first get an image as described in the previous section. Now call

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.

Support of medical image formats

MedPy relies on SimpleITK, which enables the power of ITK for image loading and saving. The supported image file formats should include at least the following. Note that not all might be supported by your machine.

Medical formats:

  • ITK MetaImage (.mha/.raw, .mhd)
  • Neuroimaging Informatics Technology Initiative (NIfTI) (.nia, .nii, .nii.gz, .hdr, .img, .img.gz)
  • Analyze (plain, SPM99, SPM2) (.hdr/.img, .img.gz)
  • Digital Imaging and Communications in Medicine (DICOM) (.dcm, .dicom)
  • Digital Imaging and Communications in Medicine (DICOM) series (/)
  • Nearly Raw Raster Data (Nrrd) (.nrrd, .nhdr)
  • Medical Imaging NetCDF (MINC) (.mnc, .MNC)
  • Guys Image Processing Lab (GIPL) (.gipl, .gipl.gz)

Microscopy formats:

  • Medical Research Council (MRC) (.mrc, .rec)
  • Bio-Rad (.pic, .PIC)
  • LSM (Zeiss) microscopy images (.tif, .TIF, .tiff, .TIFF, .lsm, .LSM)
  • Stimulate / Signal Data (SDT) (.sdt)

Visualization formats:

  • VTK images (.vtk)

Other formats:

  • Portable Network Graphics (PNG) (.png, .PNG)
  • Joint Photographic Experts Group (JPEG) (.jpg, .JPG, .jpeg, .JPEG)
  • Tagged Image File Format (TIFF) (.tif, .TIF, .tiff, .TIFF)
  • Windows bitmap (.bmp, .BMP)
  • Hierarchical Data Format (HDF5) (.h5 , .hdf5 , .he5)
  • MSX-DOS Screen-x (.ge4, .ge5)

Requirements

MedPy comes with a number of dependencies and optional functionality that can require you to install additional packages.

Main dependencies

Optional functionalities

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

License

MedPy is distributed under the GNU General Public License, a version of which can be found in the LICENSE.txt file.