Universität Wien
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300121 UE Image analysis and reconstruction of morphological datasets (2025W)

3.00 ECTS (3.00 SWS), SPL 30 - Biologie
Prüfungsimmanente Lehrveranstaltung
Moodle

An/Abmeldung

Hinweis: Ihr Anmeldezeitpunkt innerhalb der Frist hat keine Auswirkungen auf die Platzvergabe (kein "first come, first served").

Details

max. 12 Teilnehmer*innen
Sprache: Englisch

Lehrende

Termine (iCal) - nächster Termin ist mit N markiert

Mandatory preparatory meeting on Thursday 2nd, at 10:00 via Zoom (see Moodle)

  • Montag 03.11. 08:00 - 14:45 Seminarraum 1.8, Biologie Djerassiplatz 1, 1.007, Ebene 1
  • Dienstag 04.11. 09:45 - 14:45 Seminarraum 1.3, Biologie Djerassiplatz 1, 1.005, Ebene 1
  • Dienstag 04.11. 15:00 - 16:30 Seminarraum 1.8, Biologie Djerassiplatz 1, 1.007, Ebene 1
  • Mittwoch 05.11. 09:45 - 16:30 Seminarraum 1.8, Biologie Djerassiplatz 1, 1.007, Ebene 1
  • Donnerstag 06.11. 09:45 - 16:30 Seminarraum 1.8, Biologie Djerassiplatz 1, 1.007, Ebene 1
  • Freitag 07.11. 08:00 - 13:00 Seminarraum 1.3, Biologie Djerassiplatz 1, 1.005, Ebene 1
  • Freitag 07.11. 13:15 - 14:45 Seminarraum 1.6, Biologie Djerassiplatz 1, 1.011, Ebene 1

Information

Ziele, Inhalte und Methode der Lehrveranstaltung

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.

Art der Leistungskontrolle und erlaubte Hilfsmittel

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.). Also a short written summary of the main applied techniques and results will have to be written by participants.

Mindestanforderungen und Beurteilungsmaßstab

Presence during course hours is mandatory. Active participation during the trials and experimenting with the provided datasets account for 34% of the grade. A final presentation on the last day plus discussion on the methods applied accounts for the other 33%. The final protocol to be submitted will account for the last 33%

Prüfungsstoff

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

Literatur

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.

Zuordnung im Vorlesungsverzeichnis

MBO 7, MZO4, MES5

Letzte Änderung: So 28.09.2025 10:07