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SimpleITK provides a simplified interface to ITK in a variety of languages. You can either download binaries, if they are available for your platform and prefered language, or you can build SimpleITK yourself.

Additionally, there are several recommended third-party software packages.

After you have installed SimpleITK, please look to the Tutorial or the Doxygen pages for more information.

Downloading the binaries

One of the great advantages of SimpleITK is that (typically) you do not have to build it — you can simply download the binaries and get started right away!

Currently, Python binaries are available on Microsoft Windows, GNU Linux and Apple OS X. C# and Java binaries are available for Windows. We are also working towards supporting R packaging.

Python binary files

There are currently two Python binary package choices: Python Wheels, and Anaconda packages for the Anaconda Python distribution.

Use a Virtual Environment

Before you install SimpleITK we highly recommend that you create a virtual environment into which you install the package. Note that different Python versions and distributions have different programs for creating and managing virtual environments.

The use of a virtual environment allows you to elegantly deal with package compatability issues, to quote The Hitchhiker’s Guide to Python!:

A Virtual Environment is a tool to keep the dependencies required by different projects in separate places, by creating virtual Python environments for them. It solves the “Project X depends on version 1.x but, Project Y needs 4.x” dilemma, and keeps your global site-packages directory clean and manageable.

Programs for creating virtual environments include virtualenv and pyvenv for generic Python distributions, conda for the anaconda distribution, and canopy_cli for the canopy distribution.

Wheels for Generic Python Distribution

If you are working with the generic Python distribution you can install a Wheel with the lasted version of pip (>=9.0).

First thing you should do is create a virtual environment (virtualenv or pyvenv tools) and activate it.

Note: you can always manually download the wheels for your operating system and Python version directly from sourceforge.

From the command line use the pip program to install:

$ pip install SimpleITK

The pip package manager should automatically find the correct package for Microsoft Windows, Apple OS X and GNU Linux if your version and architecture are supported. If no binary package is found, then a source distribution will be downloaded and attempted to be compiled and you should look into #Build_It_Yourself.

If your pip version in the virtual environment is stale (<9.0) you need to update it (pip install -U pip).

Anaconda Distribution

First thing you should do is create a virtual environment (conda tool) and activate it.

Currently there are compiled binaries on Anaconda Cloud, easily installed with:

$ conda install -c SimpleITK

Beta and release candidate packages are also available on Anaconda cloud under the dev label:

$conda install -c SimpleITK

Enthought Canopy Distribution

First thing you should do is create a virtual environment (canopy_cli tool) and activate it.

Currently there are no binaries supported with this distribution. You will need to compile SimpleITK yourself. (Using pip installation may work.)

C# binary files

Binaries for select C# platform can be found on SimpleITK's SourceForge page. Installing the library should only involve importing the unzipped files into you C# environment. The files have the following naming convention:


Details about how to set up a C# Visual Studio project with SimpleITK can be found in the Visual Guide to SimpleITK with CSharp.

More information about getting started with a sample C# program can be found in A visual guide to building SimpleITK on Linux

Java binary files

Binaries for select Java platforms can be found on SimpleITK's SourceForge page. Installation instructions are available at a visual guide to SimpleITK in Java.

Nightly binaries

The latest binaries for the current development version of SimpleITK are also generally available. Binary packages are built as part of the nightly regression testing system. The download links are available from the CDash dashboard in the "Nightly Packages" section.

Each row on the dashboard is a SimpleITK build on a particular system, and if the build was successful there will be a package icon: package.png which links to the packages build by the system. A user may directly download the built package from such a link.

Build It Yourself

In many cases a user does not need to build SimpleITK because of the pre-built binaries available (see above). However there are several reasons a user might prefer to build SimpleITK from source:

  • The binary files for your programming language of choice are not (yet) distributed
  • You want the live on the bleeding edge by using the latest-and-greatest version of SimpleITK
  • You want to wrap your own filters using the SimpleITK infrastructure
  • You want to contribute to the development of SimpleITK
  • To use the SimpleITK's C++ interface and/or use ITK directly


To build SimpleITK you need:

  • A recent version of CMake >= 3.3 with SSL support for https.
  • A supported compiler
  • To use the latest developmental version, source code can be downloaded with git >= 1.65
    • Git is required if building SimpleITK using "SuperBuild" (see below) to automatically download the matching version of ITK, SWIG, etc...
    • Windows users may prefer msysGit
  • It is recommended to have numpy installed when testing Python bindings

Recipes / Formulas / Short Cuts

For some environments we have short cuts, scripts, for automated building of SimpleITK (see their repository for more details):

  • For the Anaconda Python distribution: The recipe and instructions for the SimpleITK build are in this GitHub repository.
  • For the Lua language with the Luarocks module deployment system, a SimpleITK rockspec is available form this GitHub repository.

Source code

If one of the above language specific front-ends are not used then SimpleITK must be build directly.

There are two options to obtain the SimpleITK source code:

  1. Download a released version from the SimpleITK SourceForge page
  2. Download the latest development version using git
git clone

Building using SuperBuild

After downloading SimpleITK's source code we STRONGLY recommend to run cmake on the SuperBuild subdirectory of SimpleITK. Execute the following commands in the parent of the SimpleITK source directory to configure the SuperBuild:

mkdir SimpleITK-build
cd SimpleITK-build
cmake ../SimpleITK/SuperBuild

The SuperBuild will automatically download and build the matching version of ITK and SWIG needed to compile SimpleITK. Additionally, it will set recommended compilation flags to minimize the size of the library and enable support for large libraries. This is the recommended way to build SimpleITK and is easiest.

If you get an error message saying that ITK_DIR is not set then, you did not correctly point cmake to the SuperBuild sub-directory. Please erase your binary directory, and point cmake to the SimpleITK/SuperBuild sub-directory.

The cmake configuration process should automatically find supported languages and enable SimpleITK wrapping for them. To manually enable a language toggle the appropriate WRAP_LANGUAGE cmake variable to ON. Verify and/or correct the advanced cmake variables to the language specific executable, libraries and include directories. For example if you have multiple Python installations ensure that all related Python variable refer to the same versions.

Then use your make utility or your cmake chosen build utility to build SimpleITK.

SimpleITK takes a while to build. Some tips and tricks to speed up development time are listed here.

Building Manually

This is not the recommended way of building SimpleITK, but it can be useful if you want to use a system version of ITK, etc. , or if you do not want to (or can not) use git.

  1. Setup the prerequisites as described above (i.e. CMake and supported compiler)
  2. Install the matching version of SWIG >= 3.0.11
  3. Download the SimpleITK source code from the SourceForge page
  4. Download the matching version of ITK, found by examining the `SuperBuild/External_ITK.cmake` file.
  5. Configure ITK using CMake
  6. Build ITK
    • Be sure to note the build settings e.g. Release x64
  7. Configure SimpleITK using CMake
    • Set ITK_DIR to the location of the ITK build location from the previous steps
  8. Build SimpleITK
    • Be sure to configure the build settings exactly the same as ITK e.g. Release x64 and CXX_FLAGS

Advanced Build Options

SimpleITK is aware of the enabled and disabled ITK Modules. It is possible to customize SimpleITK by disabling unneeded ITK modules or to extend SimpleITK by writing custom json file dependent an ITK Remote Modules being enabled.

Additionally, each of the Examples directory, and each of the Wrapping subdirectories can be configured as independent CMake project dependent on an installed SimpleITK.

A visual guide to building SimpleITK on Linux


After compilation the prudent thing to due is to test SimpleITK to ensure your build is stable and suitable for installation and use. The following commands execute the SimpleITK tests.

cd SimpleITK-build/SimpleITK-build

On Windows you will need to specify configuration. Typically that would be the Release configuration, as such:

cd SimpleITK-build/SimpleITK-build
ctest -C Release

Python installation

To install a built python package into the system Python, as root run:

 cd SimpleITK-build/Wrapping/Python
 python Packaging/ install

Alternatively, a Python virtual environment can be created and the distribution installed there.

A Python Wheel file (.whl) can be created in the "Wrapping/Python/dist" directory, by building the "dist" target. If you have used the Superbuild with the "make" generator then issue the following command:

 make -C SimpleITK-build dist

R installation

To install a built R package:

 cd SimpleITK-build/Wrapping/R/Packaging

This will install the R package "SimpleITK" in /usr/local as root or your local R installation directory.

If you are working in a multi-user environment, and are considerate of your fellow users you can install the package in a local directory:

  1. Create a local directory where you will install your R packages
 mkdir my_R_libs
  1. Add an environment variable to your .bashrc
 export R_LIBS="/path_to/my_R_libs"
  1. source your .bashrc and check the R library path, in an R shell >.libPaths()
  1. install
 cd SimpleITK-build/Wrapping/R/Packaging
 R CMD INSTALL -l /path_to/my_R_libs  SimpleITK

Recommended Software

Fiji (Fiji is Just ImageJ)

SimpleITK has a built in function, "itk::simple::Show()", which can be used for viewing images in an interactive session. Currently, this function by default Show invokes Fiji then ImageJ to display images. ImageJ was chosen because it can handle all the image types that SimpleITK supports, even 3D vector images with n components.

The Show function first searches the "PATH" environment variable, then additional standard locations are examined, if problems are encountered the correct path can be added to this environment variable and the "debugOn" option to "Show" flag set.


If ImageJ is used then we recommend downloading a recent version of ImageJ from the official home page. Recent versions come with support for the Nifti ( *.nii ) file format, which SimpleITK uses to export to ImageJ.

Note: Linux installation requires an additional step. The "Show" function searches for an executable named ImageJ or imagej, however the default tarball does not come with this file. Instead it comes with a file names script. This file contains the installation instructions. In short the file should be renamed to "imagej" and the site specific variables for the installation location, and java must be set. Also consider the "newwindow" variable... Do you really want a new instance of ImageJ launched each time you use Show? Lastly, as the installation instructions indicate, the imagej wrapper should be in your path.

iPython and Jupyter

If you are using python, ipython with Jupyter is terrific environment to perform interactive computing for image processing. With the addition of numpy and scipy, you'll have a powerful interactive environment.

We have instructional SimpleITK Jupyter Notebooks which can help you get started.