PAM3012 Digital Image Processing for Radiographers

2007-2008

Code: PAM3012
Title: Digital Image Processing for Radiographers
Instructors: Dr J.J. Moger
CATS credits: 15
ECTS credits: 7.5
Availability: B821 only
Level: 3
Pre-requisites: N/A
Co-requisites: N/A
Background Assumed: Science for Medical Imaging (PAM2011)
Duration: Weeks 11-20
Directed Study Time: 30 hours
Private Study Time: 118 hours
Assessment Tasks Time: 2 hours
Observation report: 2006/07 SJW

Aims

In this module, students will integrate theory with practice by drawing on their prior experience of imaging modalities, and re-interpreting their knowledge of imaging within a mathematical and scientific framework.

Students will develop a level of mathematical skill sufficient to analyse complex waveforms and appreciate the statistical consequences of the information stored in an image. They 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. Finally, students 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

Students should be able to:

Module Specific Skills

• show that complex waveforms can be decomposed into sinusoidal waveforms;
• discuss the implications of image perception for medical imaging;
• quantify predictive diagnostic imaging capability using various mathematical concepts;
• solve complex problems involving digital imaging systems;
• identify causes of noise in digital imaging systems and methods of minimisation;
• predict the performance of a digital imaging systems from its specifications;
• show how various image manipulation algorithms can improve the diagnostic quality of an image;
• discuss applications of image coregistration.

Discipline Specific Skills

• use mathematical skills to solve problems;
• use appropriate sources of information to develop own knowledge;

Personal and Key Skills

• manage time and prioritise workloads.

Learning and Teaching Methods

Lectures (21×1hour) and practical work (9 hours). Directed background reading.

Assignments

Three computer-based exercises (3×3hour).

Assessment

One 2-hour examination (90% Week 20) and practical work (10%).

Syllabus Plan and Content

1. Advanced mathematical skills
   1. Outline of 1D Fourier techniques: decomposition and reconstruction.
   2. Fourier techniques in 2D and 3D.
   3. Statistical concepts: distributions, variance and uncertainty.
2. Image perception
   1. Outline of visual psychophysics.
   2. Spatial and grey-scale resolution.
   3. Colour scales and colour displays.
   4. Practical considerations: brightness and contrast of display, observation distance, lighting conditions, etc...
   5. Detection of pathology: sensitivity, specificity, predictive value.
   6. Discrimination index and ROC curves.
3. Image quality
   1. Technical evaluation of images: Spatial resolution, SNR, CNR, grey-scale histograms, etc.
   2. Acceptability of images in the clinical context.
   3. Time-quality and dose-quality trade-offs.
4. Image Acquisition and Processing
   1. Analog-to-digital converters, sampling.
   2. Storage: DICOM and PACS
   3. Windowing and similar grey-scale manipulations
   4. Histogram equalisation.
   5. Spatial and frequency domain filtering
   6. Image restoration
   7. Image coregistration
   8. Volume rendering and other 3D visualisations.
5. Developments and Trends
   1. Telemedicine and Teleradiology, Health online
   2. Computerised pattern recognition
   3. Future Imaging Modalities

Core Text

Not applicable

Supplementary Text(s)

Altman D.G. (2005), Practical Statistics for Medical Research, Chapman & Hall, ISBN 1-58-488039-2 (UL: W 150 ALT)
Hader D.P. (2001), Image Analysis: Methods and Applications (2^nd edition), CRC Pres, ISBN 0-84-930239-0 (UL: On Order)

Formative Mechanisms

Students can monitor their understanding of the module by attempting practice examination questions. Students with specific problems are encouraged to approach the lecturer. In addition, students are able to monitor their own progress in the practical sessions.

Evaluation Mechanisms

The module will be evaluated using information gathered via the student representation mechanisms, the staff peer appraisal scheme, and measures of student attainment based on summative assessment.