As it’s Open Access week, I’ve decided to write a post about Open Access in the context of software, file formats and Imaging Data.
While this blog is principally about Image Analysis (turning images into numbers), Data Analysis (turning numbers into something meaningful) is also really important.
In this post I’m going to explain how to display your data in a beeswarm plot and why you might want to do this. Simple statistics are great but show me the data!
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.
In this post, we’re going to look at how to use Fiji to add annotations to an image. Arrows, asterisks and text are all added in roughly the same way. Here’s how: