DEPRECATION NOTICE: The new repository for this library can be found here <https://gitlab.inria.fr/mosaic/timagetk>_.

Tissue Image Toolkit (timagetk) is a Python library 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.

Installation

Follow the installation procedure provided with the documentation <https://virtualplants.github.io/timagetk>_.

Stable release: 1.0.0

Licence: Inria licence, non commercial use

Operaring system: Linux, Mac OS

Teams: Inria-Cirad-Inra Virtual Plants <https://team.inria.fr/virtualplants>, Inria Morpheme <http://www-sop.inria.fr/morpheme/> and Inria Project Lab Morphogenetics <https://team.inria.fr/morphogenetics/>_.