As part of the 2018 CCI Imaging Workshop, I ran a guided hands-on about using Fiji for Image Analysis. The workshop was aimed at complete novice and intermediate level users. The slides and materials were delivered through a web browser using the Reval.js framework, and this post will be a few more details on that framework and how (and why) I think it’s really useful for this sort of thing. Let’s get revealing!
As part of my job, I find myself writing lots of bits of code for people. Until quite recently, my version control system was renaming the files and commenting in the header to keep track of changes.
Not the tidiest system
I say “quite recently” as I started using git as my version control system and have not looked back. I’m by no means an expert, but in this post, I’m going to give an introduction to using git in the context of scripting.
This post is really aimed at people who have no experience with version control systems or have heard about git but have never really used it (or have tried and failed to get the hang of it as I did…twice).
A lot of work we do at the CCI uses scanning confocal microscopes, which have the advantage that the operator can pick the number of pixels in X and Y that will make up the final image.
For camera-based systems this is a less simple endeavour as the array of the CCD chip is fixed. For this reason, we may want to downsample or bin our images. In this post we’ll cover a bit of theory and details on how (and why) to bin your images.
Often, image analysis involves the measurement of objects, be they nuclei, cells or bacteria. There are plenty of good ways to select the boundaries of these objects, using freehand or segmented selections, and we’ve covered segmentation based on thresholding before.
This post is going to take a step back and look at how the magic wand tool works. It’s quick and simple, but sometimes that’s all you need. Let’s wave our wands!
In the toolbox of the image analyst, being able to correlate objects in time is a very useful skill. It opens up the doors to be able to look at dynamic changes in a system be they intensity, shape, spatial localisation or just about anything else. In this post, we’ll be covering the basic theory of object tracking and showing you how to track with open source tools.
One of the more annoying things about fluorescence imaging is that it’s a bit like trying to describe our location in the universe. There’s no absolute point of reference, the values are rather arbitrary and you rely heavily on relative measures (like being 1AU from our local star).
This post will demonstrate some of the problems with quantifying basic fluorescent images and use a case study to show how ratiometric imaging (among other things) can be used to solve them.
The Western blot is a staple of many Research Labs. Proteins are separated based on their size then labelled and identified using antibodies. Instead of using fluorescent labelled antibodies (although this can be done), most WBs use ChemiLuminescence to detect the amount of protein present. In this post we’ll look at the best way to acquire and analyse the humble Western blot.