VVZ API is not affiliated with ETH Zurich. Data might be outdated or incorrect. Please view the official ETHZ Vorlesungsverzeichnis for binding information.
Medical Image Analysis
Last Updated: 2026-06-03 00:14:07
Abstract
It is the objective of this lecture to introduce the basic concepts usedin Medical Image Analysis. In particular the lecture focuses on shaperepresentation schemes, segmentation techniques, machine learning based predictive models and various image registration methods commonly used in Medical Image Analysis applications.
Objective
This lecture aims to give an overview of the basic concepts of Medical Image Analysis and its application areas.
General Information
- Language
- English
- Levels
- BSC , MSC , NDS , WBZ
- Frequency
- Yearly recurring
Examination
- Type
- session examination
- Mode
- written 90 minutes
- Aids
- None
Course Components
| Type | Title | Time & Place | Hours |
|---|---|---|---|
| lecture with exercise | Medical Image Analysis |
|
2 h weekly |
Offered In
-
-
6th semester: third year core courses (Can be freely combined, a list of detailed recommendations is available under )
-
Specialization: Biomedical Engineering (These core courses are particularly recommended for the field of "Biomedical Engineering" but students may choose core courses from all fields freely.)
-
-
-
Biomedical Engineering Master (Only courses offered under "GESS Science in Perspective" count in this category. See "Offered in" tab in course view. For more information, please refer to )
-
-
-
Recommended Elective Courses (These courses are particularly recommended for the Bioimaging track. Please consult your track adviser if you wish to select other subjects.)
-
-
-
Track Core Courses (During the Master program, a minimum of 12 CP must be obtained from track core courses.)
-
-
-
-
-
Electives (In the ‘electives’ subcategory, at least two course units must be successfully completed. All courses listed as core courses (not electives) for one of the following ETH MSc programmes, MSc Statistics, MSc Physics, MSc Computer Science, MSc (Applied) Mathematics, MSc Neural Systems and Computation, MSc Robotics, Systems, and Control, MSc Data Science, MSc Electrical Engineering and Information Technology, can be taken as an elective course in the MSc CSE without prior permission.)
-
-
-
Application Area (Only necessary and eligible for the Master degree in Applied Mathematics. One of the application areas specified must be selected for the category Application Area for the Master degree in Applied Mathematics. At least 8 credits are required in the chosen application area. Credits from other application areas cannot be recognised for further application areas.)
-
-
-
-
Track: Signal Processing and Machine Learning (The core courses and specialization courses below are a selection for students who wish to specialize in the area of "Signal Processing and Machine Learning ", see . The individual study plan is subject to the tutor's approval.)
-
Core Courses (These core courses are particularly recommended for the field of "Signal Processing and Machine Learning". You may choose core courses form other fields in agreement with your tutor. A minimum of 24 credits must be obtained from core courses during the MSc EEIT.)
-
-
-
-
-
-
-
-
-
-
-