Tissue Image Toolkit (timagetk) is a Python language package dedicated to image processing of multicellular architectures such as plants or animals, and is intended for biologists, modelers and computer scientists.

The package provides the following main functionalities (both in 2D and 3D):

  • Linear filtering: gaussian, gradient, hessian, laplacian, etc.
  • Grayscale mathematical morphology: erosion, dilation, opening, closing, hat transform, sequential filters, etc.
  • Segmentation: h-transform, connected-component labeling, watershed, etc.
  • Registration: rigid, affine and deformable registration, composition of transformations, sequence registration, multi-view fusion etc.
  • Mathematical morphology and computation of features on labeled images: erosion, dilation, moments, spatial relationships, etc.
  • Temporal tracking based on graph-theory
  • Unit tests and examples: see the Examples

Thanks to Python language these functionalities can be combined with many other Python libraries such as for example NumPy and SciPy for scientific computing or matplotlib for curve plotting.


This is the Documentation for Tissue Image Toolkit version 1.0.0.

To build timagetk‘s dynamic documentation (sphinx), open a shell prompt and type:

  • sudo pip install -U Sphinx

Go to the timagetk/timagetk/doc/ folder and type:

  • make html

Open the file: timagetk/timagetk/build/html/index.html


There are many different ways to install Tissue Image Toolkit, and the best way depends on how you want to use it and what you already have installed. Order to help you, the Installation instructions have been detailed.


You can distribute and/or modify Tissue Image Toolkit under the terms of the Inria licence. Many people have contributed to Tissue Image Toolkit. Some of the contributors are listed in the Credits. If Tissue Image Toolkit contributes to a project that leads to a scientific publication, please acknowledge this work by Citing the project.