Join Eric Wexler for an in-depth discussion in this video Understanding image detection, part of Photoshop CS3 Extended for BioMedical Research.
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In this video we are going to be looking at image detection, both in a biological system as well as an electronic sensor. If you are following along, open up the Eye-NIH.jpeg file found in Chapter 1. Here we have a schematic of the eye, with all the part in the front basically matching any of the other acquisition equipment, where it's the lens and an area for the light to come through.
Light is focused on the sensor, which in the case of the human eye is the retina. And we are going to look at that a little bit more closely in the next picture. Here is a close up of a retina found in the back of the eye. If you are following along, you can open up RetinaRnC.psd in the Chapter 1 folder. And what we have are multiple layers, but the most important layer is the one that has the cell types that actually capture light, whether that's intensity or color wavelengths. We have the two different cell types, rods and cones.
Let's look first at the rods. Here we have them spaced out and these are the cell types that collect intensity or luminance. If there is enough light, then we also have our cones, which are able to acquire light of different wavelengths, and there are three types of cones. There are the ones that collect red, there are the ones that collect the blue wavelength of light, and the ones that collect green wavelength.
Now we are going to look how this process actually works, and we are going to compare and contrast the electronic and the biological with a real image example. If you are following along, please open NatasheGSwRT.psd, also found in the Chapter 1 folder. In this case, you want to make sure that all layers are turned on and that there is an eye for every single thing, and you can follow along turning on/off the different layers, so you can visualize on your monitor exactly what I am demonstrating.
What we have is my wonderful dog Natasha and we have here in a low light situation how our rods would work. And these are the square luminance only and we don't have any color information. Now we have talked about the retina and how it's made up of rods and cones. But now let's look at how an electronic sensor would work. What you have is a 5x5 matrix, or 25 in this case photo diodes, and each one of these just collects and registers the amount of light that falls on it.
In an 8-bit system, it would be from 0- 255 different intensity levels of light it can record. And so we can compare how we look organically using rods with how a simple CCD would acquire the same image. But working in gray scale only gives us a part of the information that's available to us in the wonderful world of color that we live in. What we would just do to actually be able to see color? If we turn off gray scale, now we can see a full color image and now we can sort of summize how an electronic sensor works in the same way that our cones work. Instead of having cells for red, green and blue, we have color filters that allow that wavelength to be measured by the photo diodes, and then using interpolation, the actual electronics will calculate for the photo diodes that are not measuring that particular wavelength. They will make basically an assumption for what that intensity would be, and that's why we can collect color information.
Now let's use Photoshop and the ability to turn on and off separate channels, so we can appreciate how a full color image is presented. We move over and we click on Channels and as we see right now, it's RGB, Red, Green and Blue. Now that we see a full color image, we are going to see how Photoshop combines the different channels. Now I want you to look at three separate areas and we will go over this in time. But we have our actual image, we have the Bayer masked, which is on the electronic sensor, and this is the way we create digital images, and then we have the Info palette.
And here we see the Info panel. This is where actual data can be found on intensity values, so that if there is 100% of red, green and blue, that would be 255, 255, 255. If there is absolutely no red, green and blue, all three of these values would be zero. Here we can see, in green it's 255, and the other two values are zero. The blue is 255, and lastly we have red showing that in the R value, up top here, it's 255 with green and blue being zero intensity for those channels. But now let's visualize this in a way that you can really understand and see how it looks.
Now let's look at the red channel. Here since the way Photoshop reports the data, we are looking at 255 and that's equal to the bright white, which shows the most amount of red, and then here we have no green and no blue. Then that would match up in this image, with the most amount of red being this white areas, or brighter gray, and in the darker gray we have less red. The same thing can be said when we look at the green only channel.
Here, again its highest intensity is green, it's pure white, and that's 255. Lesser amounts, you can see on the image, with the darkest here being only an intensity value of 24 from the green channel, versus here on the shoulder of my wonderful Natasha, it's 236. Showing a lot more intensity in the green wavelengths. And lastly, we see blue and again, for blue, these individual channels are combined to create a full colored image, but by looking at just the blue channel again, we can appreciate that there is the absence of blue in the darkest areas and that there is quite a bit of blue here around 212, a value that found in the Info panel in the shoulder, showing that this light gray area correlates to a higher amount of blue.
And then again when we turn on all the colors, we can now appreciate how Photoshop combines the individual channels, so we can appreciate a full color image.
NOTE: Actual biological research images are used for this title's examples. Some of these images, including those of internal organs and dissected animals, may be considered graphic or offensive to some viewers. Viewer discretion is strongly advised.
- Understanding imaging in biomedical research
- Getting started in Photoshop
- Organizing digital assets
- Working with image stacks
- Evaluating image color and histograms
- Modifying images for research
- Compensating for acquisition problems and limitations
- Adding reference information to images
- Sharing work
- Optimizing and creating a DICOM image or animation