Section for Image Analysis and Computer Graphics

Course 02505

Medical Image Analysis

Fall 2013


Koen Van Leemput (kvle), Associate Professor, DTU Compute, Bld. 321, room 218
phone: +45 45 25 52 95, Email:

Rasmus Larsen (rlar), Professor, DTU Compute, Bld. 324, room 113
phone: +45 45 25 34 15, Email:

Teaching Assistant

Oula Puonti (oupu), Ph.D. student, DTU Compute, Bld. 321, room 228
phone: +45 45 25 34 07, Email:

Course information

The lectures are given in building 321, room 033, on Thursdays from 1 pm – 2.30 pm (approximately). The computer exercises take place immediately after the lectures in the same room. Terminals are reserved until 5 pm; a teaching assistant will be present. You may also use the terminals outside this time-window, provided you can find a terminal that is not occupied. The computer exercises are on the topic of last week’s lecture. The computer exercises should be carried out in groups of 2-3.

Lecture notes and exercise texts and materials are available through CampusNet (one week in advance - as a rule).

How to pass this course

For each subject considered in the course an exercise is carried out and an exercise report must be prepared and handed in. The course examination consists of an oral examination in the course topics based on the course theory and exercise reports. The oral examination will be held during the period 9-21 December 2013 (the exact date will be announced later).

This is a 5 ECTS point course corresponding to a work load of 8-10 hours work per week and most of this time is used to implement the image analysis models in the exercises. Some of the algorithms are complicated and the computations can be time-consuming. You can only understand these algorithms fully by implementing them, so that you can test the effect of varying data and parameters. Hence a successful completion of the course (and exam) requires that your implementation works and that you demonstrate a good understanding of the strengths and weaknesses of the models.


No. Date Lecturer Lecture Exercise
1 5 Sep kvle Introduction; Fitting of image functions

Linear basis function models;
maximum likelihood estimation and least squares;
regularization; image smoothing;
image interpolation
No exercise
2 12 Sep kvle Landmark based registration MR bias field correction
3 19 Sep kvle Intensity based methods for registration

Sum-of-squared differences; correlation;
mutual information; principal axis transform
Landmark-based registration
4 26 Sep Tron Darvann EXCURSION: 3D laboratory. School of Dentistry No exercise
5 3 Oct kvle Non-linear deformations Mutual information based registration
6 10 Oct kvle Surface based registration non-linear intensity-based registration
7 24 Oct Kristoffer Hougaard Madsen EXCURSION: Danish Research Center for Magnetic Resonance (DRCMR) No exercise
8 31 Oct kvle surface-based segmentation

Active contour model; dynamic programming
non-linear intensity-based registration (cont.)
9 7 Nov kvle Voxel-based segmentation I

Gaussian mixture model; Markov random field priors; mean-field approximation
Surface-based registration
10 14 Nov kvle Voxel-based segmentation II

Expectation-Maximization algorithm; MR bias field correction
Dynamic programming
11 21 Nov Mads Nielsen, Biomediq Quantification of medical images for clinical trials Brain tumor segmentation
12 28 Nov kvle Atlases

Reference templates; group-wise registration; probabilistic atlases; label propagation
Expectation-Maximization algorithm
13 5 Dec kvle Validation of segmentation methods Exercise catch-up