Object-based colocalisation

Object-based colocalization
Object-based co-localisation

3D object based colocalisation

Submitted by Perrine on Fri, 02/22/2019 - 09:21

A user comes to the Facility: “I’ve got a set of 2 channels 3D images where objects are overlapping. I think the overlap might not be the same from object to object. I would like to quantify the physical overlap and get a map of quantifications”. Your mission: write the appropriate macro, knowing a user might always change her/his mind, and ask for more… Ready to take on the challenge ?


The MIPAV (Medical Image Processing, Analysis, and Visualization) application enables quantitative analysis and visualization of medical images of numerous modalities such as PET, MRI, CT, or microscopy. Using MIPAV's standard user-interface and analysis tools, researchers at remote sites (via the internet) can easily share research data and analyses, thereby enhancing their ability to research, diagnose, monitor, and treat medical disorders.


This ImageJ plug-in is a compilation of co-localization tools. It allows:

-Calculating a set of commonly used co-localization indicators:

Pearson's coefficient Overlap coefficient k1 & k2 coefficients Manders' coefficient Generating commonly used visualizations:


Having access to more recently published methods:

-Costes' automatic threshold

Li's ICA Costes' randomization Objects based methods (2 methods: distances between centres and centre-particle coincidence)

example of partial colocalisation from reference publication