Tissue Image ToolKit¶
Tissue Image Toolkit (TimageTK) 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:
Linear filter operators such as: Gaussian, gradient, hessian, laplacian, …
Morphology operator such as: erosion, dilation, opening, closing, hat transform, sequential filters, …
Linear and non-linear block-matching registration, transformation operators, sequence registration, multi-angle fusion, …
h-transform, connected component labeling, watershed, …
Stack browser, orthogonal views, projection maps and other GUI.
A simple data model allowing to perform batch processing of large of dataset.
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.
Written in Python, TimageTK leverage the work of some of the most standard Python libraries such as:
Lead developer A B CNRS
Coordinator A Inria
Coordinator C Inria
Coordinator B CNRS
Contributor A INRAe
Active research teams¶
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.
The GitHub repository of TimageTK is not maintained anymore, but is kept for historic reasons.
Some non-standard libraries, developed by other research teams, are used in this project, such as:
Follow these contribution guidelines if you want to participate to TimageTK development.