TimageTK (Tissue Image Toolkit) is a Python 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 on labeled images: erosion, dilation, etc.

For examples illustrating those functionalities, see the Examples section.

Thanks to Python language these functionalities can be combined with many other Python libraries such as NumPy and scikit-image for image processing or Matplotlib for curve plotting.

Images can be rendered using image visualization softwares, such as Gnomon or Fiji.


There are many different ways to install TimageTK, in order to help you, the Installation instructions have been detailed.

Github repository

The Github repository is not maintained anymore, but is kept for historic reasons.

Gitlab repository

The GitLab repository is maintained by members of the Mosaic team. Use the issue section to contact us if you have difficulties or issues.


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.


Follow these Contribution guidelines if you want to participate to TimageTK development.

Have a look at the Python Developer’s Guide, it never hurt to use common good practices.