The aim of this module is to give all students an understanding of the nature of digital images and how to analyse them to extract meaningful biological information. The module will bridge the gap between the acquisition of microscopy data (Foundations of Microscopy and Probes) and building models of biological systems (Image Analysis and Quantitative Modelling II).
The module will consist of a series of joint lecture and hands-on tutorial sessions taught from a computer lab. Topics covered will include the nature of digital images, image modification including discussion of valid image manipulations, segmentation, colocalization, analysis of super-resolution microscope data, diffusion measurements and machine-learning approaches. The students will learn what types of information can be extracted from digital images and what kinds of biological questions can be addressed. Importantly, they will learn how the type of acquisition performed impacts on image analysis and how to prepare data for future mathematical modelling.
- Explain the nature and properties of biological images.
- Discuss basic image analysis/manipulation methods and design a sequence of automated analysis steps
- Critically assess the outcomes, potential limitations and pitfalls of image analysis
- Implement common software for the analysis of images from advanced microscopy techniques
- Discuss the merits and drawbacks of machine-learning based approaches
The module will be delivered in the teaching areas of the Medical School and via online formats.
Semester 1 - October
- 50% unseen written exam (to assess outcomes 1, 2, 3 & 5)
- 50% practical report (to assess outcomes 3 & 4)
Academics involved in the delivery of this module
Module lead: Professor Dylan Owen
Deputy Lead: Jeremy Pike