---
# System prepended metadata

title: Leverage the powerful Insight Toolkit (ITK) in MATLAB via Python
tags: [matlab, post, itk, python]

---

# Leverage the powerful Insight Toolkit (ITK) in MATLAB via Python

###### tags: `post`, `itk`, `matlab`, `python`

###### By: Matt McCormick [![ORCID](https://info.orcid.org/wp-content/uploads/2020/12/orcid_16x16.gif)](https://orcid.org/0000-0001-9475-3756), Tom Birdsong, Brad Moore [![ORCID](https://info.orcid.org/wp-content/uploads/2020/12/orcid_16x16.gif)](https://orcid.org/0000-0003-4322-8841), Jake McCall, Stephen Aylward [![ORCID](https://info.orcid.org/wp-content/uploads/2020/12/orcid_16x16.gif)](https://orcid.org/0000-0002-7862-8856)

In recent years, [MathWork's MATLAB](https://www.mathworks.com/products/matlab.html) added [Python support](https://www.mathworks.com/help/matlab/call-python-libraries.html). 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)](https://itk.org) version 5.3 and newer and [itkwasm](https://wasm.itk.org) Python packages in MATLAB.


[ITK](https://itk.org) 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](https://www.mathworks.com/matlabcentral/fileexchange/125300-itk-matlab-python-tutorial) ([source](https://github.com/InsightSoftwareConsortium/itk-matlab-python-tutorial), [html](https://insightsoftwareconsortium.github.io/itk-matlab-python-tutorial/)).


# 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:



```matlab:Code(Display)
!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](https://www.mathworks.com/help/matlab/ref/pyenv.html) documentation.



```matlab:Code
pyenv("Version","python3")
```


```text:Output
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.



```matlab:Code
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.



```matlab:Code
!python3 pip.pyz install --user itk setuptools
```


```text:Output
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)
     ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 75.9/75.9 MB 39.0 MB/s eta 0:00:00

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)
     ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 27.3/27.3 MB 84.0 MB/s eta 0:00:00

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.



```matlab:Code
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](https://www.mathworks.com/help/matlab/matlab_external/call-user-defined-custom-module.html?searchHighlight=importlib&s_tid=srchtitle_importlib_7).




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.



```matlab:Code
% 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")
```


```text:Output
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:Code
% matlab struct
image_struct = struct(image_dict)
```


```text:Output
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]

```


```matlab:Code
spacing = double(image_struct.spacing)
```


```text:Output
spacing = 1x2    
     1     1

```


```matlab:Code
% equivalent
spacing = double(image_dict{'spacing'})
```


```text:Output
spacing = 1x2    
     1     1

```


```matlab:Code
pixels = double(image_struct.data);
imshow(pixels, [0, 255])
```


![](https://i.imgur.com/DciwtbV.png)



```matlab:Code
image_dict{'imageType'}
```


```text:Output
ans = 
  Python dict with no properties.

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

```


```matlab:Code
% Keep pixel type
pixels = uint8(image_struct.data);
imshow(pixels)
```

![](https://i.imgur.com/k12c1h7.png)


# MATLAB to ITK


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



```matlab:Code
pixels = imread("cthead1.png")
```


```text:Output
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

```


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


```text:Output
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.



```matlab:Code
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"}))
```

![](https://i.imgur.com/K6ROXO6.png)



```matlab:Code
% 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"}))
```


![](https://i.imgur.com/hHAofOL.png)


# Beyond images


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



```matlab:Code
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")
```


```text:Output
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
    

```


```matlab:Code

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


```text:Output
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}

```


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


```text:Output
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`](https://wasm.itk.org/) Python packages can be used, which leverage universal [WebAssembly](https://webassembly.org) binaries.




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



```matlab:Code
!python3 pip.pyz install --user itkwasm
```


```text:Output
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
```


```matlab:Code
itk_image = py.itk.imread("cthead1.png");
itk_image_dict = py.itk.dict_from_image(itk_image)
```


```text:Output
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)}

```


```matlab:Code
itkwasm_image = pyrun(["from itkwasm import Image", "itkwasm_image = Image(**itk_image_dict)"], "itkwasm_image", itk_image_dict=itk_image_dict)
```


```text:Output
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))

```


```matlab:Code
itkwasm_image_dict = py.dataclasses.asdict(itkwasm_image)
```


```text:Output
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)}

```


```matlab:Code
imshow(uint8(itkwasm_image_dict{"data"}))
```

![](https://i.imgur.com/s88pplx.png)



**Enjoy ITK!**


