COMP0118 Computational Modelling for Biomedical Imaging

This database contains the 2018-19 versions of syllabuses.

Note: Whilst every effort is made to keep the syllabus and assessment records correct, the precise details must be checked with the lecturer(s).

Academic session



Computational Modelling for Biomedical Imaging



Module delivery

1819/A7P/T2/COMP0118 Postgraduate

Related deliveries

1819/A7U/T2/COMP0118 Masters (MEng)

Prior deliveries




FHEQ Level


FHEQ credits



Term 2

Module leader

Alexander, Danny


Alexander, Danny

Drobnjak, Ivana

Zhang, Gary

Module administrator

Horslen, Caroline


To expose students to the challenges and potential of computational modelling in a key application area. To explain how to use models to learn about the world. To teach parameter estimation techniques through practical examples. To familiarize students with handling real data sets.

Learning outcomes

On successful completion of the module, a student will be able to:

  1. understand the aims of biomedical imaging
  2. understand the advantages and limitations of model-based approaches and data-driven approaches
  3. have knowledge of standard techniques in modelling, experimental design and parameter estimation
  4. understand the challenges of data modelling, experiment design and parameter estimation in practical situations
  5. gain knowledge of handling real-world data in computer programs.

Availability and prerequisites

This module delivery is available for selection on the below-listed programmes. The relevant programme structure will specify whether the module is core, optional, or elective.

In order to be eligible to select this module as optional or elective, where available, students must meet all prerequisite conditions to the satisfaction of the module leader. Places for students taking the module as optional or elective are limited and will be allocated according to the department’s module selection policy.

Programmes on which available:

  • MRes Robotics
  • MSc Computational Statistics and Machine Learning
  • MSc Computer Graphics, Vision and Imaging
  • MSc Data Science and Machine Learning
  • MSc Machine Learning
  • MSc Robotics and Computation
  • MRes Medical Physics and Biomedical Engineering
  • MSc Scientific Computing


There are no formal prerequisites.

The module makes heavy use of Matlab programming for courseworks, although a strong programmer in other languages will pick up the necessary Matlab during the course. It also assumes a strong grasp of general engineering mathematical concepts, in particular linear algebra, probability and statistics, geometry, and calculus.


The module introduces the basics of mathematical modelling: the distinction between models and the real world; when and how models are useful; advantages and disadvantages of explicit model-based approaches.

The module covers a range of model based approaches to biomedical imaging and basic computer science techniques that underpin them. The intention is to introduce the students to standard techniques of parameter estimation in a hands-on practical way within the context of model-based imaging. The module also gives exposure to common applications and challenges in biomedical imaging.

The content draws from examples at a range of length scales from molecular imaging, cellular scales in microscopy, regional scales, whole organ and whole population scales. The module uses each example to introduce both new kinds of model and, more fundamentally, new algorithms and techniques for parameter estimation, optimization, sampling and validation.

An indicative reading list is available via


The module is delivered through a combination of lectures and lab classes.


This module delivery is assessed as below:



Weight (%)



Project report




Coursework 1




Coursework 2




Project presentation



In order to pass this module delivery, students must achieve an overall weighted module mark of 50%.