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#40882 / #1

Seit WS 2017/18
(Deaktivierung beantragt zum WiSe 2022/23)


Medical Image Processing


Hennemuth, Anja


Mündliche Prüfung


Fakultät IV

Institut für Technische Informatik und Mikroelektronik

34341600 FG Computer Vision and Remote Sensing

No information


MAR 6-5

Hennemuth, Anja


Learning Outcomes

The course will introduce types and formats of medical image data (ultrasound, MRI, CT, histological images, …) with their characteristic properties and requirements for preprocessing. Techniques for the extraction and quantification of anatomical structures as well as functional information (motion, bloodflow, perfusion) will be introduced and explored in the exercises. Participants will learn - How spatio-temporal information is represented in medical imaging - How to segment and model anatomical structures - How to extract physiological information from 4D image data - How to implement and integrate these techniques


Segmentation of tubular structures, organs and pathological tissue with tracking, methods, morphological and classification approaches such as Gaussian mixture models and Markov random fields, deformable models; time series analysis including registration, motion tracking, (model-based) change assessment; machine learning in medical image processing

Module Components


All Courses are mandatory.

Course NameTypeNumberCycleLanguageSWSVZ
Medical Image ProcessingVLWiSeGerman2
Medical Image ProcessingUEWiSeEnglish2

Workload and Credit Points

Medical Image Processing (VL):

Workload descriptionMultiplierHoursTotal
90.0h(~3 LP)

Medical Image Processing (UE):

Workload descriptionMultiplierHoursTotal
90.0h(~3 LP)
The Workload of the module sums up to 180.0 Hours. Therefore the module contains 6 Credits.

Description of Teaching and Learning Methods

The lecture will introduce typical problems and approaches related to the extraction of information from medical image data. The relevant algorithmic approaches will be theoretically explained. Participants are expected to rehearse the content after class in preparation for the exercises. The exercises focus on the practical work at the computer in order to enable the participants to implement and explore algorithmic approaches. Homeworks will consist of specific tasks assigned to small groups and focus on theoretical questions as well as programming solutions.

Requirements for participation and examination

Desirable prerequisites for participation in the courses:

Helpful but not mandatory: Digital Image Processing or Mathematische Bildverarbeitung

Mandatory requirements for the module test application:

No information

Module completion



Type of exam

Oral exam




No information

Duration of the Module

The following number of semesters is estimated for taking and completing the module:
1 Semester.

This module may be commenced in the following semesters:

Maximum Number of Participants

The maximum capacity of students is 40.

Registration Procedures


Recommended reading, Lecture notes

Lecture notes

Availability:  unavailable


Electronical lecture notes

Availability:  unavailable



Recommended literature
No recommended literature given

Assigned Degree Programs

This module is used in the following Degree Programs (new System):

Studiengang / StuPOStuPOsVerwendungenErste VerwendungLetzte Verwendung
Biomedizinische Technik (M. Sc.)28SoSe 2021WiSe 2022/23
Computer Engineering (M. Sc.)122WS 2017/18WiSe 2022/23
Computer Science (Informatik) (M. Sc.)118WS 2017/18WiSe 2022/23
Elektrotechnik (M. Sc.)111WS 2017/18WiSe 2022/23
Wirtschaftsingenieurwesen (M. Sc.)116SS 2018WiSe 2022/23


No information