183.269 Medical Image Processing
This course is in all assigned curricula part of the STEOP.
This course is in at least 1 assigned curriculum part of the STEOP.

2023S, VO, 2.0h, 3.0EC, to be held in blocked form


  • Semester hours: 2.0
  • Credits: 3.0
  • Type: VO Lecture
  • LectureTube course
  • Format: Online

Learning outcomes

After successful completion of the course, students are able to to process data generated by medical imaging modalities and to develop algorithms for processing the data, analyzing the observed structures and quantifying disease- and treatment-relevant markers. They are able to identify and implement modern methods of machine learning that are optimal for problems concerning computer-aided diagnosis, prognosis, and the prediction of disease progression or treatment effects. After positive completion of the course, students are able to implement algorithms for segmentation, model-based detection, texture analysis, interactive segmentation, rigid and non-rigid registration, and functional imaging analysis.

Subject of course

We will discuss the following topics in the course of the lecture:

  • Medical imaging modalities
  • Segmentation (active contours, level-set)
  • Model based detection and segmentation of anatomical structures (active shape models, active appearance models)
  • Texture analysis
  • Interactive segmentation (graph cuts)
  • Rigid and non-rigid registration
  • Neuroimaging and machine learning, analysis of neuroimaging data
  • Applications in interoperative / interventional visualization
  • Atlas building

Methods and modalities will be explained based on real world cases. For each we will discuss the mathematical bassics, and ways of solving it. For each unit we will distribute reading material, so that we can have an interesting discussion during the lecture.

In the course of the lab exercise 183.630 we will implement and test selected methods on medical imaging data.


Teaching methods

This semester the course will be held online via TUWEL and Zoom. All relevant information including slides and links to the online lectures will be provided via TUWEL.

The course consists on the one hand of a detailed discussion of methodical approaches for image acquisition and analysis, and on the other hand, of algorithmic solutions developed in interactive discussions based on case studies. On the one hand, the basics are taught, on the other hand, the ability to combine these methods to an effective solution approach is acquired, which starts from a problem description (e.g. detection of a tumor, quantitative tracking of disease and treatment progression, examination of large groups of patients, use of algorithms in clinical practice).

Mode of examination




Course dates

Fri15:00 - 17:0028.04.2023EI 8 Pötzl HS - QUER Lecture Kick-Off
Fri16:00 - 18:0005.05.2023EI 8 Pötzl HS - QUER Lecture MedBV
Thu15:00 - 18:0025.05.2023EI 11 Geodäsie HS - GEO Lecture MedBV
Tue15:00 - 17:0013.06.2023EI 9 Hlawka HS - ETIT Lecture MedBV
Tue16:00 - 19:0020.06.2023EI 8 Pötzl HS - QUER Lecture MedBV
Thu15:00 - 18:0022.06.2023EI 11 Geodäsie HS - GEO Lecture MedBV
Fri16:00 - 18:0023.06.2023Hörsaal 6 - RPL Lecture MedBV
Thu17:00 - 19:0029.06.2023EI 9 Hlawka HS - ETIT (LIVE)Exam MedBV
Course is held blocked

Examination modalities

Written Exam


DayTimeDateRoomMode of examinationApplication timeApplication modeExam
Wed17:00 - 19:0006.03.2024 EI5 - https://www.tuwien.at/tu-wien/organisation/zentrale-bereiche/gebaeude-und-technik/veranstaltungsservice-und-lehrraumsupport/raumdatenbank/hoersaele/ei-5-hochenegg-hoersaalwritten19.02.2024 09:00 - 04.03.2024 09:00TISSMedBV Exam
Wed17:00 - 19:0015.05.2024FAV Hörsaal 1 Helmut Veith - INF written29.04.2024 09:00 - 13.05.2024 09:00TISSMedBV Exam

Course registration

Begin End Deregistration end
06.03.2023 08:00 24.03.2023 23:59 24.03.2023 23:59


Study CodeObligationSemesterPrecon.Info
066 453 Biomedical Engineering Not specified
066 645 Data Science Mandatory elective
066 932 Visual Computing Mandatory elective
066 936 Medical Informatics Mandatory1. Semester


No lecture notes are available.

Accompanying courses