Good morning everybody.

Today we will continue the discussion

of defect pixel interpolation algorithms.

Before we do so, let me briefly summarize where we are

and what we are currently doing

to make sure that you understand

the storyline of this lecture.

Diagnostic medical image processing.

We have clarified the term diagnostics.

That means that you capture images

and you have time to analyze the images to do a diagnosis.

So there are no strong time constraints

like in the summer semester

when we talk about interventional image processing.

What did we discuss so far?

Well, we are still quite at the beginning of this semester.

So we did not do that much,

but we have considered two important topics

in the context of acquisition specific pre-processing.

Specific pre-processing.

So on the way from the detector to the monitor,

we want to do some image processing on the fly.

And within this chain of image processing algorithms,

we want to apply methods that take care

of the acquisition procedure and eliminate artifacts

that are implied by the acquisition procedure.

And what's meant by this becomes clear

if you look at the image intensifiers

and the technology that is used to convert X-ray image

into an X-ray signal into a digital image,

you need an image intensifier that converts the energy

of the incoming photons into intensity values, basically.

And we have seen that the setup of image intensifiers

has a vacuum tube or uses a vacuum tube.

We know that there are electrodes involved,

that we have an electron optics

that amplifies the electrons that are generated

by the stream of X-ray particles.

And having electrons in motion causes problems

if we move or if the electrons move in a magnetic field.

And we have the problem that we have the Earth magnetic field.

And that, of course, causes distortions.

And these distortions, deviations of the electrons

have to be corrected, caused by the Earth magnetic field.

And what does that mean, basically?

Well, straight lines in real life

are mapped to curved lines.

Think about a needle that is used for punctuation procedure.

You see the needle curved in the image due to image distortions.

And we learned how to do that by using undistortion functions.