MSc Health Data Science / Course details

Year of entry: 2024

Course unit details:
Mathematical Computing for Medical Imaging

Course unit fact file
Unit code IIDS67462
Credit rating 15
Unit level FHEQ level 7 – master's degree or fourth year of an integrated master's degree
Teaching period(s) Semester 2
Available as a free choice unit? No


This unit will introduce mathematical and computing techniques used in medical image analysis.

It will cover optimsation techniques, statistical shape modelling, classification/regression and an introduction to neural networks.  Examples from a range of medical image analysis problems will be included.  

Students will learn how the different algorithms work, but more importantly, when to use each approach.  The course includes both the mathematical techniques and an introduction to Python libraries which implement them.  Students will learn Python programming skills in order to write software to solve practical problems.




Students are assumed to have a basic knowledge of linear algebra (matrices and vectors) and basic calculus (e.g. derivatives).  Introductory material covering what is assumed is included on-line.


  • Introduce concepts of programming in key areas of Medical Image Computing
  • Introduce key mathematical and statistical concepts and methods appropriate to the quantitative analysis of medical images.

Learning outcomes

  • Understand the key mathematical concepts and computing tools for extracting information from medical images.
  • Have developed skills in evaluation of algorithms for the purposes of understanding publications in this area and so contribute to the available pool of new researchers.


Teaching and learning methods

12 3-hour sessions, each of which will involve a mixture of lectures, problem solving and in-class practical programming.

Exercises will be given at the end of each lecture. All material will be made available via Blackboard.


Knowledge and understanding

  • Become familiar with a range of mathematical techniques for optimisation, classification, modelling and image analysis

Intellectual skills

  • Selecting the most appropriate software tools to solve a problem
  • Data analysis and reporting of results

Practical skills

  • Familiarity with widely used mathematical techniques and Python implementations thereof.
  • Python programming skills

Employability skills

Analytical skills
Problem solving
Written communication
Programming (Python) Skills

Assessment methods

Method Weight
Other 40%
Written exam 60%

Two Python programming assignments (20% each)
One final written exam (60%)

Feedback methods

  • Formal summative assessments 
  • Real time educative formative assessments during practical classes

Study hours

Independent study hours
Independent study 150

Teaching staff

Staff member Role
Timothy Cootes Unit coordinator

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