Welcome to the winter semester,

Diagnostic Medical Image Processing.

And this semester we will focus

on medical image processing algorithms

that are used for diagnostic purposes.

And I will use the first lecture to introduce to you

what does it mean diagnostics and what is the core concept

that is basically underlying all the methods

that we are using.

Here are a few questions regarding the course mechanics.

Most of them are already answered.

This is a question that shows up always.

Can we shift the time for the lectures?

Thank you very much, no.

Is diagnostic medical image processing required

to attend part two?

I would say, yes, it's better if you attended,

but if you're highly motivated, it's doable.

And I guess a few of you are here

who attended already the second part.

Do I need to know MATLAB or OCTAV?

Well, it's better to know it,

but if you are not familiar with it,

you will learn this during the exercise courses.

And is there a book that covers the topics of the lecture?

No, still it's waiting to be written.

And well, I plan to do that with Markus now,

Markus Prümer, but let's see whether we will find the time

or whether we will drift this again for 10 years or so.

So the topics that we are covering here.

And it's important for me that you know

the storyline of the lecture,

that you always have a good feeling where we are,

where we come from basically and where we are heading to.

So you should always keep in mind a big picture

what's going on in the lecture.

Well, the guidelines for the lecture,

well, for me, it's important that I am unique in its content.

So I try to explain to you some special methods

that are important for today's industry,

for today's research,

and it should not be a standard lecture

that you can find at any Fachhochschule

or any other university, should be special.

Then I also try to challenge you.

I mean, if you read the comments in the web

about the lecture, there are sometimes statements saying,

ooh, if you wanna do it the hard way, attend this lecture.

I want to challenge you,

but of course I do not try to overload you