Okay, so welcome back everybody to interventional medical image processing.

We started talking a bit about different pre-processing algorithms and what we wanted to aim at are

algorithms that are fast in particular and so far we have seen that we can cut down on

the complexity of algorithms and then end up with several algorithms that have the ability

to be executed very fast.

Later, we also want to interpret images and in order to interpret images we will make

use of low-level descriptors that we then can compose to different structures and extract

information from the image.

So later we may want to detect certain features of the image and often what we do is we do

some kind of processing to simplify the information extraction.

One thing that will be very important are edges, so edges and gradients.

Edges and gradients are very useful in order to detect important points in an image and

we will talk today about something which is called the structure tensor and why the structure

tensor is interesting to extract features from an image.

So why are edges and gradients interesting?

So, if you think about an image, most of the time the points that will pop up in your eye

immediately are changes in intensity.

So if you, for example, think of a slice image, what you will note is that if you have some

kind of, let's say you have a slice of a patient, don't I have a, yeah I have an example here.

So let's say you have a slice of a patient image, so this is a CT slice and this one

is contrast enhanced.

You will see that you have homogeneous areas in the image, so there's homogeneous areas

like here and then there are certain changes in intensity and these changes in intensity

are in particular interesting.

And on the right hand side you see the gradient image.

So the gradient image and here the gradient norm is denoted in color, shows you the important

features of the image.

So you can see that, for example, the table here appears very bright and furthermore the

outline of the patient, so at the boundary of the patient you have a strong change in

gradient, so there is a change in the intensity and this pops up in the gradient image.

So the gradient image is related to edges and edges are interesting because let's say

you want, for example, to detect the outline of an organ, so you want to determine the

surface of the liver or something like this, then you can see that the organ has an edge,

an intensity edge surrounding and you can use this edge information to determine the

outline.

Or also here you can see that the different organs, they are outlined by edges.

Okay, then what can we further see?

There's a very strong edge from air to the patient because the patient is composed mostly

of water, so soft tissues, and you have a strong edge from air to soft tissue and there's

also a very strong edge again here.

Why do we have the strong edge here?

Can you imagine?

Yes?

Well, this appears dark in this image, so what could be here?

There's another strong edge here.

Can you see the strong edge here, down here?

What's this?

Yeah, so this is part of the spine bone and the bone is much denser than the soft tissue,

so you have a very strong edge here.

But here you also have a very strong edge.