Tissue Image ToolKit

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

Overview

The package provides the following main functionalities:

Image Filtering

Linear filter operators such as: Gaussian, gradient, hessian, laplacian, …

Mathematical Morphology

Morphology operator such as: erosion, dilation, opening, closing, hat transform, sequential filters, …

Image Registration

Linear and non-linear block-matching registration, transformation operators, sequence registration, multi-angle fusion, …

Image Segmentation

h-transform, connected component labeling, watershed, …

Visualisation

Stack browser, orthogonal views, projection maps and other GUI.

Batch Processing

Simple JSON-based data structure to batch process large data sets.

Organisation

Jonathan Legrand
Lead developer A B CNRS

Christophe Godin
Coordinator A Inria

Grégoire Malandain
Coordinator C Inria

Teva Vernoux
Coordinator B CNRS

Guillaume Baty
Contributor

Guillaume Cerutti
Contributor A INRAe

Sophie Ribes
Contributor

Active Research Teams

A Inria team Mosaic, RDP-ENS Lyon, UMR5667.

B Hormonal Signalling and Development team, RDP-ENS Lyon, UMR5667.

C Inria team Morpheme, Sophia Antipolis.

Source Code Repository

Inria GitLab Repository

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

GitHub Repository

The GitHub repository of TimageTK is not maintained anymore, but is kept for historic reasons.

Contributing

We welcome all contributions to the development of TimageTK as it is intended as an open-source scientific software.

Have a look at the contribution guidelines in the documentation for more details.

Additional Notes

Images and other data structures can be rendered using the visualization functions we provide. However, if you are less experienced with code, using a dedicated interactive image visualization software such as Gnomon or Fiji is recommended.

TimageTK leverages the work of some of the most standard Python libraries:

For 3D visualisations, we rely on:

Other less “standard” libraries, developed by research teams, are used: