Image Processing and Quantitative Data Analysis

URL study guide

https://studiegids.vu.nl/en/courses/2025-2026/XM_0085

Course Objective

Knowledge and understanding:About the basic principles and modalities of several important bioimaging techniques and applications About different types of imaging data sets About different types of image processing and data analysis techniques About different types of software languages and tools for analysing image datasetsApplying knowledge:To be able to handle imaging datasets using ImageJ, Python and Maltab/Octave To be able to use and write scripting of analysis pipelines in relevant programming languages To be able to apply image processing techniques To be able to extract and analyse quantitative information from images To be able to apply knowledge from literature or other available sourcesMaking judgements:To select and validate the relevant image processing methods and tools in a correct way Based on a formulated bioimage-related research question, develop an approach, generate results, and interpret these results within the context of the research question Search, select and test methods found in the literature or other available sources Communication:To write a report and/or to give a presentation about a research question, the analysis and results in a group of students. Learning skills:To be able to understand and implement new methods from literature To create new approaches for specific applications and or research questions

Course Content

In biomedical and biological research many different (functional) imaging techniques exist, including fluorescence and optical microscopy (bright field, phase contrast, etc.), scanning electron microscopy (SEM), X-ray, ultrasound (sonography), magnetic resonance (MRI), computerised tomography (CT), positron emission tomography (PET). With these techniques we can visualise and study biological systems at the molecular, cellular, multicellular and tissue level in space and time. The datasets produced by these techniques are digital images and can be further used to extract quantitative information. The analysis of these 2D/3D or even higher dimensional datasets requires the use of image processing techniques. In this course we will cover topics about quantitative bioimaging techniques and image analysis techniques including basic operations, region of interests, feature extraction, masks and the use of filters. Furthermore we will cover more advanced processing techniques including image segmentation, spectral unmixing and color deconvolution, image registration, localisation and tracking, co-localisation. We will discuss and apply these techniques also within the context of widely used biomedical and biological applications, e.g. immunohistology, FISH, super resolution, FRAP, FRET, fluorescent labelling, cell tracking, enzymatic activity etc. As a subsequent step quantified spatio-temporal information, for example about intensities, locations, timings and other parameters will be further statistically analysed, e.g. auto
- and cross-correlation, point statistics, fitting, comparison and clustering.

Teaching Methods

About 8 lectures (2-3 two-hour lectures per week), exercises during/in between lectures and practicals About 8 computer practicals (2-3 afternoon sessions per week), feedback (theoretical and practical) will be given during and after the computer practical sessions Self-study Final project week presentation and critical discussion of state-of-the-art

Method of Assessment

6 practical assignments [~30%], individual or group depending on the number of course studentsDigital exam [~40%], individualProject week assignment with presentation [~30%], groupFurther assessment and grading details will be posted on Canvas (resits and compensation rules)

Literature

provided at the start or during the courseLecture slides provided at the start or during the coursePractical training material provided at the start or during the courseSoftware provided at the start or during the course

Target Audience

M Bioinformatics and Systems Biology

Additional Information

Students are required to arrange and bring a laptop

Entry Requirements

Bachelor in any science discipline with sufficient programming background

Recommended background knowledge

Programming experience e.g. Python, ImageJ, Matlab