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?
In the last post, we looked at the following image to decide if an intensity of 2000 at the tip of the arrowhead was bright or dim. Without knowing the bit depth of the image it’s impossible to tell.
We posed that if this was a 12bit image, that’s fairly bright (2000/4096) but if it’s a 16bit image, then it’s very dim (2000/65536). But why would these two conditions look the same? The answer lies in the histograms and transfer functions…
In the list of things that everyone who uses a microscope should know, this has to be near the top and yet I find a surprising number of people are either never taught it or don’t fully grasp the idea of Bit Depth. In this post and Part 2, we will deal with (almost) everything you need to know about bit depth, dynamic range and image histograms.
One of the most frequent questions that I get asked is how to add a scale bar to an image. While it’s good that people are adding scale bars to images for presentations, posters and papers, it’s not always done terribly well. In this post, we’ll look at two ways to do it and why you would favour one or the other