Unless your imaging facility is in a clean room (and you never touch it), from time to time, we all end up with unsightly splotches on our transmitted light images. The best fix for this is to clean the microscope but sometimes you just have to do what you can with what you’ve got.
Thankfully there’s a fairly easy way to correct it post-acquisition. Let’s Flat-field correct!
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).
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!
Whenever you’re testing a new analysis protocol or playing around with some software, it’s always handy to have some sample data to mess with. But what if you don’t yet have the data, or what if you need more, or need more specific data? In this post, we’re going to delve into the world of synthetic data by making a sample tracking data set.
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.
Replication is key to the Scientific method. This is especially true when dealing with imaging, when a single field (at any magnification) is rarely representative of the entire experiment, let alone the underlying phenomenon as a whole. The easy solution to this is to take many fields of many samples, but visualising and manipulating a whole dataset can be a time consuming endeavour. In this post, we’re going to cover some of the tricks for working with a dataset in Fiji and take the first steps into the big wide world of scripting.
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.