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The University of Southampton

ISVR6138 Biomedical Application of Signal and Image Processing

Module Overview

During the process of diagnosis and subsequent treatment, patients routinely undergo imaging, measurement and monitoring procedures using a wide range of techniques. Whether it is the automated monitoring of blood pressure of flow, the electrical signals generated during the contractions of the heart or medical images taken with a state of the art medical scanner, all these techniques produce vast amounts of data, for example in the form of time-series signals representing blood-pressure variation or the large image data-sets from a medical scanner. To help medical practitioners make sense of this flood of information, it is thus becoming increasingly important to provide reliable computational tools that can automatically enhance, analyse and monitor these signals and images, and extract (or facilitate the extractin of) clinically useful information. The same is true in medical and biological research, where similar biomedical monitoring techniques are used to study both healthy biological functions as well as mechanisms of disease and where ever larger studies collect ever larger data-sets of signals and images. Signal and image processing techniques now allow us to predict unobserved biological processes from non-invasive measurements (for example in the control of blood flow), identify specific impairments (for example in executing movements of the limb), reliably screen large populations for common medical conditions (such as breast cancer) and allow us to automatically compare physiological properties between different populations (such as, for example, the change in the size of certain brain regions in epilepsy patients). In this module students will study a range of signal and image processing techniques and will learn how they can be used to analyse a range of biomedical signals and images. Whilst learning general and specific analysis techniques, you will also gain insight into relevant biomedical background (such as the basic physiological properties that give rise to many biomedical signals and images) and many of the engineering principles that underlie the operation of key devices that are used to record biomedical signals or generate biomedical images. The module will also discuss engineering issues in the wider context of exploiting engineering for health-care, including relevant ethical and economic issues and multidisciplinary collaboration and communication. Students should be aware that some knowledge of signal processing or control is strongly recommended.

Aims and Objectives

Learning Outcomes

Knowledge and Understanding

Having successfully completed this module, you will be able to demonstrate knowledge and understanding of:

  • The origins and characteristics of some of the most commonly used biomedical signals, including ECG, EMG, evoked potentials, and blood pressure
  • Sources and characteristics of noise and artefacts in these signals.
  • Use of these signals in diagnosis, patient monitoring and physiological investigation.
  • The application of signal processing techniques, including digital filtering, coherent averaging, spectral estimation, cross-correlation, input-output modelling with biomedical signals, and the interpretation of results.
  • Different approaches and options in common biomedical signal processing tasks, such as the estimation of evoked potentials, automatically detecting heart-beats from the ECG, and physiological modelling.
  • The physical basis and engineering principles underlying common approaches in acquiring 2D and 3D images for biomedical applications, including x-ray imaging, tomographic techniques, Magnetic Resonance Imaging (MRI) and ultrasound imaging.
  • Different analysis techniques used to automatically process and analyse these images, including different image representations, image enhancement and restoration and edge detection, automatic image segmentation and registration.
  • Issues of multidisciplinary research and communication at the interface between engineering and medicine/health-care and biology.
  • An awareness of relevant ethical, legal and economic issues.
Transferable and Generic Skills

Having successfully completed this module you will be able to:

  • Read and interpret current research literature and summarise results obtained.
  • Acquire a working knowledge of new software applications and IT resources.
  • Implement specified algorithms by computer programming.
  • Discuss technical issues within a multidisciplinary environment.
  • Communicate results, conclusions and problems to health-care professionals (such as clinicians and clinical scientists) with limited mathematical or engineering background.
Subject Specific Practical Skills

Having successfully completed this module you will be able to:

  • Implement key biomedical signal and image processing tasks in computer programes
  • Select and evaluate appropriate signal and image processing methods for specific biomedical applications, and interpret results critically
  • Synthesize results, discuss interpretations, and propose alternative algorithms.
Disciplinary Specific Learning Outcomes

Having successfully completed this module you will be able to:

  • Describe the origin and characteristics of some biomedical signals and images.
  • Explain how appropriate signal processing methods are being applied to these signals and images for clinical practice and biomedical research, together with the rationale for their use.
  • Implement some signal and image-processing algorithms and interpret the results appropriately.
  • Critically analyse different signal and image processing approaches, identify some of their strengths and limitations, and formulate alternatives.


• Physiological origin of some biomedical signals, such as ECG, EEG, EMG, blood pressure and blood flow. Recording techniques, characteristic features and sources of artefact and noise in these signals. • Analogue and digital data acquisition. • Deterministic and random signals. • Signal processing and analysis tools: - Digital filtering. - Coherent averaging. - Spectral estimation. - Cross correlation. - Coherence. - Linear models of systems. • Applications of signal processing such as for - Noise reduction in biomedical signals. - Evoked potentials. - Measuring heart-rate. - Control of heart-rate, blood pressure and blood flow. • Challenges in diagnosis and patient monitoring - Gold standards. - Specificity, sensitivity and repeatability of diagnostic procedures. - Between and within subject variability. • Major Biomedical Imaging Modalities - X-rays. - Computed Tomography. - Magnetic Resonance Imaging (MRI). - Ultrasound. • Biomedical Image Analysis Techniques - Image representations and transforms. - Image filtering, enhancement and restoration. - Edge detection, segmentation and registration. • Societal contexts - Technologies in healthcare. - Fundamental of ethical and legal frameworks in biomedical engineering. - Introduction to the technology lifecycle and technology assessment for biomedical technologies.

Learning and Teaching

Teaching and learning methods

Lectures, Labs, Tutorials, Demonstrations,Visits to labs (if available), Programming assignments/mini-projects.

Practical classes and workshops9
Preparation for scheduled sessions10
Completion of assessment task45
Wider reading or practice10
Total study time150

Resources & Reading list

Computer requirements. Hands on programing for Signal & Image Processing





MethodPercentage contribution
Continuous Assessment 60%
Final Assessment  40%


MethodPercentage contribution
Set Task 100%


MethodPercentage contribution
Set Task 100%

Repeat Information

Repeat type: Internal & External

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