Universität Wien
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300121 UE Image analysis and reconstruction of morphological datasets (2024W)

3.00 ECTS (3.00 SWS), SPL 30 - Biologie
Continuous assessment of course work
Moodle

Registration/Deregistration

Note: The time of your registration within the registration period has no effect on the allocation of places (no first come, first served).

Details

max. 10 participants
Language: English

Lecturers

Classes (iCal) - next class is marked with N

VORBESPRECHUNG: 01.10.24, 15:00 (Zoom, moodle)

  • Monday 04.11. 09:45 - 14:45 Seminarraum 1.8, Biologie Djerassiplatz 1, 1.007, Ebene 1
  • Tuesday 05.11. 09:45 - 16:30 Seminarraum 1.1 PC, Biologie Djerassiplatz 1, 1.003, Ebene 1
  • Wednesday 06.11. 09:45 - 16:30 Seminarraum 1.8, Biologie Djerassiplatz 1, 1.007, Ebene 1
  • Thursday 07.11. 09:45 - 16:30 Seminarraum 1.1 PC, Biologie Djerassiplatz 1, 1.003, Ebene 1
  • Friday 08.11. 09:45 - 14:45 Seminarraum 1.8, Biologie Djerassiplatz 1, 1.007, Ebene 1

Information

Aims, contents and method of the course

The course will address image theory, especially what are images and how images can be changed or modified. The acquired skills will particularly focus on image processing and visualization methods of morphological datasets. Open source software such as FIJI (Image J) or drishti will be used during the course. The course does not require any previous knowledge on image processing, but a general interest in the field is expected of the students.
Aim of the course is to convey foundations and methods of image theory, image processing and visualization of morphological datasets. After the course, students should independently be able to use image filters, and also conduct quantitative and qualitative analyses of morphological datasets.

Assessment and permitted materials

Active participation, analysis and understanding of the sample datasets and demonstrations during the course represent one of the main evaluation criteria. Students will receive short projects in groups that will require the acquired skills for analysis (filtering of datasets, volume and surface calculations, segmentation and visualization of datasets). Own projects of e.g. master or phD students are possible. Die achieved result of the short projects will be evaluated in a short oral examination (methods, results, etc.)

Minimum requirements and assessment criteria

Presence during course hours is mandatory. Active participation during the trials and experimenting with the provided datasets account for 50% of the grade. A final presentation on the last day plus discussion on the methods applied accounts for the other 50%

Examination topics

Comprehension of the various programs and image processing and analysis tools. Presentation at the end of the course.

Reading list

Specific references will be provided during the course. Since we only use open source software, various forum of image analysis websites provide the best help.

Association in the course directory

MBO 7, MZO4, MES5

Last modified: Mo 23.09.2024 11:47