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.
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.
Sampling and more specifically, sampling frequency is a really important and much misunderstood concept in many fields of research. As we’ll see in this analogy-ridden post, it’s important to understand sampling in both time and space.
In previous posts, we’ve almost taken for granted how Fiji/ImageJ deals with multidimensional data. Whenever you have more than one image, be it a second channel all the way up to massive 5D datasets, you’re dealing with stacks.
This concept is so fundamental, I thought it deserving of it’s own post. Let’s stack ’em up.
In the last post, I covered a few ways to visually present your data, The astute reader may have noticed that the single channels were always presented in greyscale, while colour was saved for the merged or composite channels.
This is one of those gripes that make me sound like a broken record, but you can open just about any article with imaging and find an example of it. Why do otherwise sensible people present single-channel data in colour?