Labs

The labs cover selected image processing methods. For each method, the students are provided with a Jupyter notebook that contains a brief explanation and a template for the implementation. The students are tasked to follow the template and complete the implementation.

Attendance at the labs is recommended but not mandatory.

See information about the semester work.

Schedule

Week	Date	Topic
1	21. 9.	Introduction: B-splines
2	28. 9.	Holiday
3	5. 10.	Active contours
4	12. 10.	Shape transforms
5	19. 10.	Canceled
6	26. 10.	Superpixel segmentation
7	2. 11.	Graph cut
8	9. 11.	Deep learning: U-Net
9	16. 11.	Deep learning: U-net cont.
10	23. 11.	Deep learning: classification
11	30. 11.	Frangi vesselness filter
12	7. 12.	Image registration
13	14. 12.	Image registration with SimpleITK
14	11. 1.	Semester work: presentation

Required software

Although the students can use the lab computers, they are encouraged to use their own devices, which will allow them to work on the assignments at home. The easiest way to install all required software is through the Conda system. Use the Miniconda installer to obtain a Conda installation.

After installing Conda, create a new environment and install the required dependencies:

conda create -n zmo python=3 jupyter matplotlib numpy

To verify the installation, activate the fresh environment

conda activate zmo

and check that the following commands exit successfuly:

python -c "import numpy; import matplotlib"
jupyter notebook --version

It is necessary to activate the Conda environment before using Jupyter, so it is required to run

conda activate zmo

before working on the assignments.

Table of Contents

Labs

Schedule

Required software