Digital Image Processing for Radiographers
| Module title | Digital Image Processing for Radiographers |
|---|---|
| Module code | PAM3012 |
| Academic year | 2024/5 |
| Credits | 15 |
| Module staff | Dr Clare Thorn (Convenor) Dr Beth McGill (Convenor) |
| Duration: Term | 1 | 2 | 3 |
|---|---|---|---|
| Duration: Weeks | 11 |
| Number students taking module (anticipated) | 78 |
|---|
Module description
In this module, you will integrate theory with practice by drawing on your prior experience of imaging modalities, and re-interpreting your knowledge of imaging within a mathematical and scientific framework.
You will develop a level of mathematical skill sufficient to analyse complex waveforms and appreciate the statistical consequences of the information stored in an image. You will develop a knowledge of the underlying algorithms used by image manipulation tools and the extent to which the use of these affect the qualities of the image.
Module aims - intentions of the module
Through undertaking this module, you will learn how each and every component of the imaging chain, from presentation of patient through to the interpretive skills of the radiographer/radiologist can affect the predictive diagnostic capabilities of a method.
Intended Learning Outcomes (ILOs)
ILO: Module-specific skills
On successfully completing the module you will be able to...
- 1. Show that complex waveforms can be decomposed into sinusoidal waveforms;
- 2. Discuss the implications of image perception for medical imaging;
- 3. Quantify predictive diagnostic imaging capability using various mathematical concepts;
- 4. Solve complex problems involving digital imaging systems;
- 5. Identify causes of noise in digital imaging systems and methods of minimisation;
- 6. Predict the performance of a digital imaging systems from its specifications;
- 7. Show how various image manipulation algorithms can improve the diagnostic quality of an image;
- 8. Discuss applications of image co-registration;
- 9. Discuss applications of Artificial Intelligence (AI) in medical imaging.
ILO: Discipline-specific skills
On successfully completing the module you will be able to...
- 10. Use mathematical skills to solve problems;
- 11. Use appropriate sources of information to develop own knowledge;
ILO: Personal and key skills
On successfully completing the module you will be able to...
- 12. Manage time and prioritise workloads.
- 13. Demonstrate basic problem-solving skills;
Syllabus plan
Whilst the module’s precise content may vary from year to year, an example of an overall structure is as follows:
Advanced mathematical skills
Outline of 1D Fourier techniques: decomposition and reconstruction.
Fourier techniques in 2D and 3D.
Statistical concepts: distributions, variance and uncertainty.
Image perception
Outline of visual psychophysics.
Spatial and grey-scale resolution.
Colour scales and colour displays.
Practical considerations: brightness and contrast of display, observation distance, lighting conditions, etc...
Detection of pathology: sensitivity, specificity, predictive value.
Discrimination index and ROC curves.
Image quality
Technical evaluation of images: Spatial resolution, SNR, CNR, grey-scale histograms, etc.
Acceptability of images in the clinical context.
Time-quality and dose-quality trade-offs.
Image Acquisition and Processing
Analog-to-digital converters, sampling.
Storage: DICOM and PACS
Windowing and similar grey-scale manipulations
Histogram equalisation.
Spatial and frequency domain filtering
Image restoration
Image co-registration
Volume rendering and other 3D visualisations.
Developments and Trends
Telemedicine and Teleradiology, Health online
Computerised pattern recognition
Future Imaging Modalities
Learning activities and teaching methods (given in hours of study time)
| Scheduled Learning and Teaching Activities | Guided independent study | Placement / study abroad |
|---|---|---|
| 17 | 133 | 0 |
Details of learning activities and teaching methods
| Category | Hours of study time | Description |
|---|---|---|
| Scheduled learning and teaching activities | 11 | Seminars |
| Scheduled learning and teaching activities | 6 | 3x2-hour computer practicals |
| Guided independent study | 122 | Reading, private study and revision |
| Reading, private study and revision | 11 | Directed online learning activities |
Formative assessment
| Form of assessment | Size of the assessment (eg length / duration) | ILOs assessed | Feedback method |
|---|---|---|---|
| Computer-based | 3 x 2 hour lab session | 1-13 | Marks returned and discussed in tutorials |
Summative assessment (% of credit)
| Coursework | Written exams | Practical exams |
|---|---|---|
| 0 | 100 | 0 |
Details of summative assessment
| Form of assessment | % of credit | Size of the assessment (eg length / duration) | ILOs assessed | Feedback method |
|---|---|---|---|---|
| Exam 1 | 40 | 1 hour | 1-12 | Marks returned and discussed in tutorials |
| Exam 2 | 60 | 90 minutes | 1-12 | iExeter and ELE |
| 0 | ||||
| 0 | ||||
| 0 | ||||
| 0 |
Details of re-assessment (where required by referral or deferral)
| Original form of assessment | Form of re-assessment | ILOs re-assessed | Timescale for re-assessment |
|---|---|---|---|
| Exam 1(40%) | Exam 1(40%) | 1-12 | May assessment period |
| Exam 2 (60%) | Exam 2 (90 minutes) | 1-12 | August/September assessment period |
Re-assessment notes
In the event of a student deferring the in-class test, a separate test would be sat as soon as practically possible, or at the latest during the May assessment period.
In the event of a student failing the module as a result of failure of either the in-class test or written examination, or both, the referral assessment will be a single written examination that makes up 100% of the module mark.
In the event of a student deferring the written examination, they will sit the same examination as the referred students, but this will be combined with their in-class test to make their final module mark.
Indicative learning resources - Basic reading
Not applicable
Indicative learning resources - Web based and electronic resources
Indicative learning resources - Other resources
- Dougherty, G. (2009), Digital Image Processing for Medical Applications,Cambridge University Press, ISBN 9780511532528
- Gonzalez R.C. and Woods R.E. (2017), Digital Image Processing (4th edition), Pearson Education, ISBN 9781292223049
- Oakley J. (2006), Digital Imaging, A Primer for Radiographers, Radiologists and Health Care Professionals, Cambridge University Press, ISBN 1-841-10121-4
| Credit value | 15 |
|---|---|
| Module ECTS | 7.5 |
| Module pre-requisites | PAM1020, PAM2013 |
| NQF level (module) | 6 |
| Available as distance learning? | Yes |
| Origin date | 01/09/2004 |
| Last revision date | 16/08/2023 |
