itk-matlab-python-tutorial

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A tutorial on how to use ITK in MATLAB via Python

https://insightsoftwareconsortium.github.io/itk-matlab-python-tutorial/

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ITK and MATLAB Python Tutorial

Introduction

In recent years, MathWork's MATLAB added Python support. This unlocks the capabilities of the powerful open source scientific Python ecosystem inside MATLAB. This post provides a tutorial on how to use Insight Toolkit (ITK) version 5.3 and newer and itkwasm Python packages in MATLAB.

ITK is an open-source, cross-platform library that provides developers with an extensive suite of software tools for image analysis. Developed through extreme programming methodologies, ITK builds on a proven, spatially-oriented architecture for processing, segmentation, and registration of scientific images in two, three, or more dimensions.

To interactively reproduce this tutorial, run the corresponding MATLAB Live Script (source, html).

Installation

This section walks through how to install the itk Python package into the PythonEnvironment available in the MATLAB Online environment.

In general, you can install itk into your local Python environment by simply calling:

!python -m pip install itk

Since MATLAB Online uses Linux, we will use python3 to specify the MATLAB Python version. On Windows, you may want to specify a version with'Version', '3.10'or similar. For more information, see the pyenv documentation.

pyenv("Version","python3")

ans = 
  PythonEnvironment with properties:

          Version: "3.8"
       Executable: "/usr/bin/python3"
          Library: "libpython3.8.so.1.0"
             Home: "/usr"
           Status: Loaded
    ExecutionMode: InProcess
        ProcessID: "5004"
      ProcessName: "MATLAB"

Next, we will Install itk via the Python package manager, pip. A standard Linux Python distribution is unusal in that it does not ship with pip, so we will download a self-contained version in this example.

websave("pip.pyz", "https://bootstrap.pypa.io/pip/pip.pyz");

For MATLAB Online, install the package into set the user packages and the setuptools package.

!python3 pip.pyz install --user itk setuptools

Collecting itk
  Downloading itk-5.3.0-cp38-cp38-manylinux2014_x86_64.manylinux_2_17_x86_64.whl (8.3 kB)
Collecting setuptools
  Downloading setuptools-67.4.0-py3-none-any.whl (1.1 MB)
     ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 1.1/1.1 MB 80.0 MB/s eta 0:00:00

Collecting numpy
  Downloading numpy-1.24.2-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (17.3 MB)
     ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 17.3/17.3 MB 109.4 MB/s eta 0:00:00

Collecting itk-filtering==5.3.0
  Downloading itk_filtering-5.3.0-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (75.9 MB)
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Collecting itk-registration==5.3.0
  Downloading itk_registration-5.3.0-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (27.3 MB)
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Collecting itk-io==5.3.0
  Downloading itk_io-5.3.0-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (26.2 MB)
     ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 26.2/26.2 MB 73.7 MB/s eta 0:00:00

Collecting itk-numerics==5.3.0
  Downloading itk_numerics-5.3.0-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (60.0 MB)
     ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 60.0/60.0 MB 48.3 MB/s eta 0:00:00

Collecting itk-core==5.3.0
  Downloading itk_core-5.3.0-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (83.6 MB)
     ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 83.6/83.6 MB 35.1 MB/s eta 0:00:00

Collecting itk-segmentation==5.3.0
  Downloading itk_segmentation-5.3.0-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (17.2 MB)
     ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 17.2/17.2 MB 106.7 MB/s eta 0:00:00

Installing collected packages: setuptools, numpy, itk-core, itk-numerics, itk-io, itk-filtering, itk-segmentation, itk-registration, itk
  WARNING: The scripts f2py, f2py3 and f2py3.8 are installed in '/home/mluser/.local/bin' which is not on PATH.
  Consider adding this directory to PATH or, if you prefer to suppress this warning, use --no-warn-script-location.
Successfully installed itk-5.3.0 itk-core-5.3.0 itk-filtering-5.3.0 itk-io-5.3.0 itk-numerics-5.3.0 itk-registration-5.3.0 itk-segmentation-5.3.0 numpy-1.24.2 setuptools-67.4.0

For MATLAB Online, we need to add the user site packages to sys.path so Python will find them.

usp = py.site.getusersitepackages();
pyrun(["import sys", "if usp not in sys.path: sys.path.append(usp)"], usp=usp);
py.sys.path;

If the Python interpreter has already been started, we would need to manually import the Python package for MATLAB.

Again, these steps are unnecessary for local installations where the itk Python package is simply installed before starting MATLAB.

ITK to MATLAB

In this section, we will show how to load an image with ITK and display it with MATLAB.

% download a test image
websave("cthead1.png", "https://bafybeigja4wbultavomsvai433hln7uqabzl2mg24frxzqblx4y4cvd5am.ipfs.w3s.link/ipfs/bafybeigja4wbultavomsvai433hln7uqabzl2mg24frxzqblx4y4cvd5am/cthead1.png");

% load the image with itk
pyrun(["import itk", "image = itk.imread('cthead1.png')"])

% transfer python dict to matlab
image_dict = pyrun(["image_dict = itk.dict_from_image(image)"], "image_dict")

image_dict = 
  Python dict with no properties.

    {'imageType': {'dimension': 2, 'componentType': 'uint8', 'pixelType': 'Scalar', 'components': 1}, 'name': '', 'origin': (0.0, 0.0), 'spacing': (1.0, 1.0), 'size': (256, 256), 'direction': array([[1., 0.],
           [0., 1.]]), 'data': array([[0, 0, 0, ..., 0, 0, 0],
           [0, 0, 0, ..., 0, 0, 0],
           [0, 0, 0, ..., 0, 0, 0],
           ...,
           [0, 0, 0, ..., 0, 0, 0],
           [0, 0, 0, ..., 0, 0, 0],
           [0, 0, 0, ..., 0, 0, 0]], dtype=uint8)}

% matlab struct
image_struct = struct(image_dict)

image_struct = 
    imageType: [1x1 py.dict]
         name: [1x0 py.str]
       origin: [1x2 py.tuple]
      spacing: [1x2 py.tuple]
         size: [1x2 py.tuple]
    direction: [1x1 py.numpy.ndarray]
         data: [1x1 py.numpy.ndarray]

spacing = double(image_struct.spacing)

spacing = 1x2    
     1     1

% equivalent
spacing = double(image_dict{'spacing'})

spacing = 1x2    
     1     1

pixels = double(image_struct.data);
imshow(pixels, [0, 255])

image_dict{'imageType'}

ans = 
  Python dict with no properties.

    {'dimension': 2, 'componentType': 'uint8', 'pixelType': 'Scalar', 'components': 1}

% Keep pixel type
pixels = uint8(image_struct.data);
imshow(pixels)

MATLAB to ITK

In this section, we will show how to load an image with MATLAB and transfer it to ITK.

pixels = imread("cthead1.png")

Warning: PNG library warning: sCAL: invalid unit.
pixels = 256x256 uint8 matrix    
   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0
   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0
   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0
   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0
   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0
   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0
   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0
   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0
   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0
   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0

pyrun(["import numpy as np", "array = np.asarray(pixels)"], pixels=pixels)
pyrun(["image = itk.image_view_from_array(array)", "print(image)"])

Image (0x7f3aec3261e0)
  RTTI typeinfo:   itk::Image<unsigned char, 2u>
  Reference Count: 1
  Modified Time: 402
  Debug: Off
  Object Name: 
  Observers: 
    none
  Source: (none)
  Source output name: (none)
  Release Data: Off
  Data Released: False
  Global Release Data: Off
  PipelineMTime: 0
  UpdateMTime: 0
  RealTimeStamp: 0 seconds 
  LargestPossibleRegion: 
    Dimension: 2
    Index: [0, 0]
    Size: [256, 256]
  BufferedRegion: 
    Dimension: 2
    Index: [0, 0]
    Size: [256, 256]
  RequestedRegion: 
    Dimension: 2
    Index: [0, 0]
    Size: [256, 256]
  Spacing: [1, 1]
  Origin: [0, 0]
  Direction: 
1 0
0 1

  IndexToPointMatrix: 
1 0
0 1

  PointToIndexMatrix: 
1 0
0 1

  Inverse Direction: 
1 0
0 1

  PixelContainer: 
    ImportImageContainer (0x7f3aec3219f0)
      RTTI typeinfo:   itk::ImportImageContainer<unsigned long, unsigned char>
      Reference Count: 1
      Modified Time: 397
      Debug: Off
      Object Name: 
      Observers: 
        none
      Pointer: 0x7f3aec2a0d80
      Container manages memory: false
      Size: 65536
      Capacity: 65536

Call ITK filters

Let's call an itk filter and look at the result.

pyrun(["smoothed = itk.discrete_gaussian_image_filter(image, sigma=sigma)"], sigma=3.0);
smoothed = pyrun(["smoothed = itk.dict_from_image(smoothed)"], "smoothed");
imshow(uint8(smoothed{"data"}))

% equivalent
smoothed = py.itk.discrete_gaussian_image_filter(py.itk.image_from_dict(image_dict), sigma=3.0);
smoothed = py.itk.dict_from_image(smoothed);
imshow(uint8(smoothed{"data"}))

Beyond images

ITK supports other data structures beyond images, including Mesh's, PointSet's, and spatial Transform's.

websave("cow.vtk", "https://bafybeihqh2e2xyff5fzwygx4m2lmgqmnm3kv7gyzjgtcgv7kpfvvjy4rty.ipfs.w3s.link/ipfs/bafybeihqh2e2xyff5fzwygx4m2lmgqmnm3kv7gyzjgtcgv7kpfvvjy4rty/cow.vtk");

% load the mesh with itk
% pyrun(["mesh = itk.meshread('cow.vtk')"])
mesh = py.itk.meshread("cow.vtk")

mesh = 
  Python itkMeshF3 with properties:

    thisown: 1
       this: [1x1 py.SwigPyObject]

    Mesh (0x7f3aee68b400)
      RTTI typeinfo:   itk::Mesh<float, 3u, itk::DefaultStaticMeshTraits<float, 3u, 3u, float, float, float> >
      Reference Count: 1
      Modified Time: 10117
      Debug: Off
      Object Name: 
      Observers: 
        none
      Source: (none)
      Source output name: (none)
      Release Data: Off
      Data Released: False
      Global Release Data: Off
      PipelineMTime: 669
      UpdateMTime: 10116
      RealTimeStamp: 0 seconds 
      Number Of Points: 2903
      Requested Number Of Regions: 1
      Requested Region: 0
      Buffered Region: 0
      Maximum Number Of Regions: 1
      Point Data Container pointer: 0
      Size of Point Data Container: 0
      Number Of Points: 2903
      Number Of Cell Links: 0
      Number Of Cells: 3263
      Cell Data Container pointer: 0
      Size of Cell Data Container: 0
      Number of explicit cell boundary assignments: 3
      CellsAllocationMethod: itk::MeshEnums::MeshClassCellsAllocationMethod::CellsAllocatedDynamicallyCellByCell


% transfer python dict to matlab
mesh_dict = py.itk.dict_from_mesh(mesh)

mesh_dict = 
  Python dict with no properties.

    {'meshType': {'dimension': 3, 'pointComponentType': 'float32', 'pointPixelComponentType': 'float32', 'pointPixelType': 'Scalar', 'pointPixelComponents': 1, 'cellComponentType': 'uint64', 'cellPixelComponentType': 'float32', 'cellPixelType': 'Scalar', 'cellPixelComponents': 1}, 'name': '', 'numberOfPoints': 2903, 'points': array([3.71636 , 2.34339 , 0.      , ..., 4.23234 , 1.90308 , 0.534362],
          dtype=float32), 'numberOfPointPixels': 0, 'pointData': array([], dtype=float32), 'numberOfCells': 3263, 'cells': array([  4,   4, 250, ..., 966, 961, 970], dtype=uint64), 'numberOfCellPixels': 0, 'cellData': array([], dtype=float32), 'cellBufferSize': 18856}

% x,y,z point positions
points = double(mesh_dict{"points"})

points = 1x8709    
    3.7164    2.3434         0    4.1266    0.6420         0    3.4550    2.1699         0    3.9293    0.4117         0    3.2474    2.0733         0    3.7317    0.1864         0    2.9854    1.9827         0    3.4860   -0.1417         0    2.7235    1.8988         0    3.3352   -0.3758         0    2.3396    1.8077         0    3.1181   -0.7039         0    2.1281    1.7884         0    2.0422    1.7772         0    1.4567    1.7859         0    2.9709   -0.9597         0    2.9318   -1.1297

WebAssembly packages with itkwasm

In addition to native Python packages, itkwasm Python packages can be used, which leverage universal WebAssembly binaries.

Calls are similar, but we use Python's builtin dataclasses.asdict for conversion.

!python3 pip.pyz install --user itkwasm

Collecting itkwasm
  Downloading itkwasm-1.0b74-py3-none-any.whl (18 kB)
Collecting wasmer
  Downloading wasmer-1.1.0-cp38-cp38-manylinux_2_24_x86_64.whl (1.6 MB)
     ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 1.6/1.6 MB 89.3 MB/s eta 0:00:00

Requirement already satisfied: numpy in /home/mluser/.local/lib/python3.8/site-packages (from itkwasm) (1.24.2)
Collecting wasmer-compiler-cranelift
  Downloading wasmer_compiler_cranelift-1.1.0-cp38-cp38-manylinux_2_24_x86_64.whl (1.9 MB)
     ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 1.9/1.9 MB 115.2 MB/s eta 0:00:00

Collecting typing-extensions
  Downloading typing_extensions-4.5.0-py3-none-any.whl (27 kB)
Installing collected packages: wasmer-compiler-cranelift, wasmer, typing-extensions, itkwasm
Successfully installed itkwasm-1.0b74 typing-extensions-4.5.0 wasmer-1.1.0 wasmer-compiler-cranelift-1.1.0

itk_image = py.itk.imread("cthead1.png");
itk_image_dict = py.itk.dict_from_image(itk_image)

itk_image_dict = 
  Python dict with no properties.

    {'imageType': {'dimension': 2, 'componentType': 'uint8', 'pixelType': 'Scalar', 'components': 1}, 'name': '', 'origin': (0.0, 0.0), 'spacing': (1.0, 1.0), 'size': (256, 256), 'direction': array([[1., 0.],
           [0., 1.]]), 'data': array([[0, 0, 0, ..., 0, 0, 0],
           [0, 0, 0, ..., 0, 0, 0],
           [0, 0, 0, ..., 0, 0, 0],
           ...,
           [0, 0, 0, ..., 0, 0, 0],
           [0, 0, 0, ..., 0, 0, 0],
           [0, 0, 0, ..., 0, 0, 0]], dtype=uint8)}

itkwasm_image = pyrun(["from itkwasm import Image", "itkwasm_image = Image(**itk_image_dict)"], "itkwasm_image", itk_image_dict=itk_image_dict)

itkwasm_image = 
  Python Image with properties:

         data: [1x1 py.numpy.ndarray]
         size: [1x2 py.tuple]
       origin: [1x2 py.tuple]
    imageType: [1x1 py.itkwasm.image.ImageType]
         name: [1x0 py.str]
      spacing: [1x2 py.tuple]
     metadata: [1x1 py.dict]
    direction: [1x1 py.numpy.ndarray]

    Image(imageType=ImageType(dimension=2, componentType='uint8', pixelType='Scalar', components=1), name='', origin=(0.0, 0.0), spacing=(1.0, 1.0), direction=array([[1., 0.],
           [0., 1.]]), size=(256, 256), metadata={}, data=array([[0, 0, 0, ..., 0, 0, 0],
           [0, 0, 0, ..., 0, 0, 0],
           [0, 0, 0, ..., 0, 0, 0],
           ...,
           [0, 0, 0, ..., 0, 0, 0],
           [0, 0, 0, ..., 0, 0, 0],
           [0, 0, 0, ..., 0, 0, 0]], dtype=uint8))

itkwasm_image_dict = py.dataclasses.asdict(itkwasm_image)

itkwasm_image_dict = 
  Python dict with no properties.

    {'imageType': {'dimension': 2, 'componentType': 'uint8', 'pixelType': 'Scalar', 'components': 1}, 'name': '', 'origin': (0.0, 0.0), 'spacing': (1.0, 1.0), 'direction': array([[1., 0.],
           [0., 1.]]), 'size': (256, 256), 'metadata': {}, 'data': array([[0, 0, 0, ..., 0, 0, 0],
           [0, 0, 0, ..., 0, 0, 0],
           [0, 0, 0, ..., 0, 0, 0],
           ...,
           [0, 0, 0, ..., 0, 0, 0],
           [0, 0, 0, ..., 0, 0, 0],
           [0, 0, 0, ..., 0, 0, 0]], dtype=uint8)}

imshow(uint8(itkwasm_image_dict{"data"}))

Enjoy ITK!

Cita come

McCormick, Matthew, et al. “ITK: Enabling Reproducible Research and Open Science.” Frontiers in Neuroinformatics, vol. 8, Frontiers Media SA, 2014, doi:10.3389/fninf.2014.00013.

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Versione	Pubblicato	Note della release
1.0.2.0	24 feb 2023	See release notes for this release on GitHub: https://github.com/InsightSoftwareConsortium/itk-matlab-python-tutorial/releases/tag/v1.0.2	Scarica
1.0.1.0	24 feb 2023	See release notes for this release on GitHub: https://github.com/InsightSoftwareConsortium/itk-matlab-python-tutorial/releases/tag/v1.0.1	Scarica
1.0.0	24 feb 2023		Scarica

Per visualizzare o segnalare problemi su questo componente aggiuntivo di GitHub, visita GitHub Repository.

MLA	McCormick, Matthew, et al. “ITK: Enabling Reproducible Research and Open Science.” Frontiers in Neuroinformatics, vol. 8, Frontiers Media SA, 2014, doi:10.3389/fninf.2014.00013.
APA	McCormick, M., Liu, X., Jomier, J., Marion, C., & Ibanez, L. (2014). ITK: enabling reproducible research and open science. Frontiers in Neuroinformatics, 8. Frontiers Media SA. Retrieved from https://doi.org/10.3389%2Ffninf.2014.00013
BibTeX	@article{McCormick_2014, doi = {10.3389/fninf.2014.00013}, url = {https://doi.org/10.3389%2Ffninf.2014.00013}, year = 2014, publisher = {Frontiers Media {SA}}, volume = {8}, author = {Matthew McCormick and Xiaoxiao Liu and Julien Jomier and Charles Marion and Luis Ibanez}, title = {{ITK}: enabling reproducible research and open science}, journal = {Frontiers in Neuroinformatics} }