ImageC is an open source project initiated as a leisure project by Joachim Danmayr and Melanie Schürz in cooperation with the Paris Lodron University of Salzburg and the Ludwig Boltzmann Institute for Nanovesicular Precision Medicine.

Inspired by ImageJ a J AVA written image processing tool for single image processing, ImageC is a high throughput image processing tool written in C++.

ImageC is fully open-source under the AGPL-3.0 license and its development takes place on GitHub in the joda01/imagec repository. ImageC is available as a free version under this license for non commercial use.

• Use the ImageC forum to discuss your new feature idea.
• Once accepted one of the ImageC contributors will create an github issue linking to the forum post.

• Have a look to the ImageC forum, probably your question is still answered there.
• If not open a new thread in the forum with the imagec tag.

If you are really sure you found a bug you can directly create an issue on the github issues page.

No! ImageC is designed having a low performance footprint and can also be executed on a normal home PC or Laptop. For the minimum hardware requirements refer to the section Resources and limits.

There are many factors which influences the processing speed of your pipelines. Here are a few rules of thumb:

• Bigger images needs longer to analyze.
• The longer the pipeline, the longer the analysis time.
• The more objects detected in the image, the longer the analysis time.
• Rolling ball, Watershed and Median rank filter are commands which costs a lot of time.
• Classical object segmentation is faster than AI object detection.

ImageC tries to use all your available CPU cores to run the analysis. Using a PC with multiple cores can significantly increase processing time.

ImageC was developed in a group of biologists specialized on nano vesicle. Therefore one of the main use cases for ImageC is identifying spots in fluorescence microscopy images, counting them and calculate the colocalization of them.

However, ImageC is highly flexible and can be used in fields other than biology. For instance, it could be used in astronomy to count the stars in the sky.

In a nutshell in every application you have the need to automatically quantify images and automate this process.