Section for Image Analysis and Computer Graphics

Course 02505

Medical Image Analysis

Fall 2014

Lecturers

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

Teaching Assistant

Mikael Agn (miag), Ph.D. student, DTU Compute, Bld. 321, room 215
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 the current 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 8-22 December 2014 (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.

Calendar

No. Date Lecturer Lecture Exercise
1 4 Sep kvle Introduction; Fitting of image functions (Course note pp. 1-14)

Linear basis function models;
maximum likelihood estimation and least squares;
regularization; image smoothing;
image interpolation
I. MR bias field correction
2 11 Sep kvle Landmark based registration (Course note pp. 15-24) II. Landmark-based registration
3 18 Sep Kristoffer Hougaard Madsen EXCURSION: Danish Research Center for Magnetic Resonance (DRCMR) No exercise
4 25 Sep kvle Intensity based methods for registration (Course note pp. 25-32)

Sum-of-squared differences; correlation;
mutual information; principal axis transform
III. Mutual information based registration
5 2 Oct kvle Non-linear deformations (Course note pp. 32-46) IV. Non-linear intensity-based registration
6 9 Oct TBA Guest lecture IV. Non-linear intensity-based registration (cont.)
7 23 Oct kvle Surface based registration (Course note pp. 59-63) V. Surface-based registration
8 30 Oct Tron Darvann EXCURSION: 3D laboratory. School of Dentistry No exercise
9 6 Nov kvle Surface-based segmentation (Course note pp. 63-63)

Active contour model; dynamic programming
VI. Dynamic programming
10 13 Nov kvle Voxel-based segmentation I (Course note pp. 71-81)

Gaussian mixture model; Markov random field priors; mean-field approximation
VII. Brain tumor segmentation
11 20 Nov kvle Voxel-based segmentation II (Course note pp. 82-93)

Expectation-Maximization algorithm; MR bias field correction
VIII. Expectation-Maximization algorithm
12 27 Nov kvle Atlases (Course note pp. 101-109)

Reference templates; group-wise registration; probabilistic atlases; label propagation
Exercise catch-up
13 4 Dec kvle Course wrap-up No exercise