Updated: May 13
A few weeks ago, we posted a poll on Twitter to our followers and the bioimage analysis community. The question was simple: “What would you like to learn more about?”
The results were decisive (and surprising):
As a result, we’re glad to introduce an ongoing series of tips to help you with image analysis using ImageJ and Python scripting!
These quick tips are:
Short form, blog-style posts
Focused on specific tasks (as opposed to describing entire programs or workflows)
Related somehow to image analysis problems
These quick tips are not:
Comprehensive, from-the-ground-up tutorials
Exclusive to Aivia users
You can think of these tips as the opposite of the blog posts for recipes you find on Pinterest:
The code is provided up front so you can quickly peruse it on your own without reading our lengthy ruminations.
Next, the code is described on a line-by-line basis so you can get clarification on parts that you may find perplexing.
Finally, some additional thoughts are provided in a longer form if you are curious to know why certain things are done certain ways.
What if I don’t know ImageJ or Python scripting at all?
Keeping it concise means that we can’t go into too much detail about every single character in our code. If you are completely new to ImageJ or Python scripting, there are already some great resources where you can learn from the ground up.
The Python Bible on Udemy is a great online course for learning the basics, and the instructor is an absolute delight.
Automate the Boring Stuff is very popular for its practical approach.
Sreenivas Bhattiprolu has a massive, comprehensive YouTube playlist for learning Python specifically for microscopy applications.
The Royal Microscopy Society maintains a list containing many resources for Python as related to microscopy.
Papers to read and cite when using the software (see here also):
ImageJ: Schneider, C. A.; Rasband, W. S. & Eliceiri, K. W. (2012), "NIH Image to ImageJ: 25 years of image analysis", Nature methods 9 (7): 671-675, PMID 22930834.
Fiji: Schindelin, J.; Arganda-Carreras, I. & Frise, E. et al. (2012), "Fiji: an open-source platform for biological-image analysis", Nature methods 9 (7): 676-682, PMID 22743772.