Written on 13.11.23 by Pascal Peter

Tomorrow's lecture is planned to be conducted in hybrid mode in E1.3 HS001 as originally planned and will be streamed online/recorded simultaneously.

Written on 07.11.23 by Pascal Peter

Reminder: For the time being, the lecture format will remain online only. Please keep an eye out for announcements on Teams.

IAM Introductory lecture on 31.10. will be ONLINE ONLY

Written on 30.10.23 by Pascal Peter

Due to illness, the first lecture will be online only. You can join in Teams. Hopefully we can return to the regular format as soon as possible.

Written on 19.10.23 by Pascal Peter

Exam dates in the welcome flyer have been updated.

Image Acquisition Methods


For basic information at a glance consult our welcome flyer.

Motivation: The course is designed as a supplement for image processing lectures, to be attended before, after or parallel to them. In order to choose the right image processing methods for a given image, it is important to know what the image data represents and what specific properties it possesses.

Teaching Goals: Therefore, in this lecture, participants learn:

  • what digital images are,
  • how they are acquired,
  • what they encode and what they mean,
  • which limitations are introduced by image acquisition.

Contents: A broad variety of image acquisition methods is described, including imaging by virtually all sorts of electromagnetic waves, acoustic imaging, magnetic resonance imaging and more. While medical imaging methods play an important role, the overview is not limited to them.

Hybrid Lectures: Participate in person or on MS Teams. Recordings will be made available. 

Tue 16:15-18:00, E1.3, HS001
First lecture: 31.10.2023


Entrance Requirements

Basic mathematics courses are recommended.
Basic knowledge in physics is helpful, but the lecture is designed to be self-sufficient in this regard.



Assignments are designed for group work. You are encouraged to connect with your fellow students and solve the problems together. The lecturer will be available to assist you and check your solutions. For all assignments, a written solution is also offered online.

If you have questions concerning the tutorials, please do not hesitate to contact Pascal Peter.


Assessments / Exams

There will be two exams, one takes place at the end of the lecture period and a second one just before the start of the next semester. The exams are planned as written closed book exams and dates will be announced closer to the beginning of the lecture period.

You can find the detailed rules for our exams in the self test assignment in the Teams file repository.
You can participate in both exams, and the better grades counts. Please remember that you have to register online for the exam in the HISPOS system of the Saarland University.

If you cannot attend the exam, contact Pascal Peter as early as possible. In case you have proof that you cannot take part for medical reasons or you have another exam on the same day, we can offer you an oral exam as a replacement. Note that we need written proof (e.g. a certificate from a physician/Krankenschein) for the exact date of the exam.

Lecture notes / Assignments

Lecture content in form slides and assignments are available for download online. Access will be granted after registration. In addition, we will provide pre-recoreded lecture videos. Note that the initial registration requires manual confirmation and can thus be delayed a bit.

The assignments and the source code needed for the programming assignments will be provided here during the semester.

  • B. Jähne, H. Haußecker, P. Geißler, editors, Handbook of Computer Vision and its Applications. Volume 1: Sensors and Imaging. Academic Press, San Diego 1999.
  • S. Webb, The Physics of Medical Imaging. Institute of Physics Publishing, Bristol 1988.
  • C. L. Epstein, Introduction to the Mathematics of Medical Imaging. Pearson, Upper Saddle River 2003.
  • R. Blahut, Theory of Remote Image Formation. Cambridge University Press, 2005.
  • A. C. Kak, M. Slaney, Principles of Computerized Tomographic Imaging. SIAM, Philadelphia 2001.
  • Articles from journals and conferences.

Further references will be given during the lecture.

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