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2017/18 Undergraduate Module Catalogue
MEDP3518 Medical Image Analysis
10 creditsClass Size: 12
Module manager: Dr S Sourbron
Email: s.sourbron@leeds.ac.uk
Taught: Semester 2 (Jan to Jun) View Timetable
Year running 2017/18
Pre-requisite qualifications
Applicants must have successfully completed at least two years undergraduate MBChB or BChD training or international equivalent; for international medical students proof of English Language proficiency will be required; a minimum or IELTS 7.0 with no component under 6.5 or equivalent.or
This module is available as a discovery module and would be suitable for students who are studying a physical science degree (or related subject).
This module is mutually exclusive with
| MEDP2320 | Diagnostic Image Analysis: Better, Faster, Cheaper |
| MEDP2321 | Diagnostic Image Analysis |
| MEDP5318M | Medical Image Analysis |
This module is approved as a discovery module
Module summary
This module will describe the principles and role of computer image processing and analysis in medical imaging. It covers both the underlying theory and provides students with practical experience of these techniques applied to medical images using a computer image processing package.Objectives
To provide an understanding of the principles of medical image processing and give practical experience realising them using an image processing package.To provide an appreciation of how medical image analysis contributes to current practice and research in medical imaging.
Learning outcomes
On completion of this module, students should be able to:
1. demonstrate knowledge and understanding of the main categories of Image Processing operation and be able to recognise and describe individual image processing
2. understand the principles of key concepts used in medical image processing; demonstrate the ability to evaluate and select image processing operations for a particular task;
3. critically evaluate alternative methods for performing a particular medical image analysis task;
4. demonstrate the ability to develop an image processing algorithm which combines a range of image processing operations to solve a given medical image task;
5. employ good practice when undertaking medical image work with regard to planning, execution and data protection;
6. recognise and understand how (and why) image processing techniques can be applied across all medical imaging modalties;
7. critically analyse recent developments related to medical imaging or visualisation;
8. identify and understand how artifacts on diagnostic images can alter the viewers' perception and the problems associated.
Skills outcomes
Ability to employ image processing and analysis techniques appropriate to medical imaging, having both an understanding of the theoretical background and practical experience.
Syllabus
Digital Images
1) Define the formats of computer based digital images
2) Computer number storage and implications for digital imaging
3) Image formation, sampling and re-sampling image matrices
4) Principles of image storage
5) Introduce geometric image manipulations.
6) Digital image formation, sampling, spatial and temporal resolution.
7) Display of a digital image
8) Data protection legislation and data security
Introduction to Medical Image Analysis
1) Define some basic terms used in image processing
2) Understand the brightness and spatial properties of digital images
3) Recognise a range of image processing operations.
Visualisation
1) Understand the advantages and disadvantages of different visualisation methods
2) Define scene-based and object-based visualisation methods
3) Give examples of alternative ways of viewing data as slices
4) Understand how pixel dimensions affect the appearance of slices
5) 3D rendering
6) Compare and contrast maximum intensity projection (MIP) with surface rendering
7) Give examples of the cues used to give an impression of three dimensionality.
Segmentation and Classification
1) Understand image enhancement using the histogram
2) Define the image analysis technique of segmentation
3) Give examples of manual, automatic and semi-automatic methods of segmentation
4) Discuss factors affecting reproducibility of segmentation methods
5) Define the image analysis technique of classification
6) Give examples of methods of classification
7) Give examples of clinical problems where the analytical techniques might be applied.
Image Transforms
1) Introduce common image transforms including Fourier and Hough transforms.
2) Discuss applications of the image transforms.
Filtering Images
1) Identify the difference between distinct block and sliding neighbourhood filtering operations.
2) Give examples and understand rank order filters (e.g. maximum, average, median and sharpening filters).
3) Define the image enhancement technique of convolution filtering both in the spatial and Fourier domains.
4) Perform a simple convolution on a matrix in both Fourier and Spatial domains.
5) Discuss factors affecting the choice of filter and method of application.
6) Recognise the effect of the simplest filters on clinical images.
Image Arithmetic and Morphology
1) Perform logical operations on binary images.
2) Identify the different arithmetic operations that can be applied to images and why these are useful.
3) Perform shape-based morphological operations on medical images and understand their applications.
Registration
1) List a number of applications for image synthesis
2) Define the steps of image registration
3) Define and distinguish between the four types of image transformation.
Medical Image Analysis in Clinical Practice
1) Computer Aided Diagnosis
2) Image processing and analysis in radiotherapy planning
3) Quantitative imaging.
Teaching methods
| Delivery type | Number | Length hours | Student hours |
| Lecture | 19 | 1.00 | 19.00 |
| Practical | 3 | 2.00 | 6.00 |
| Tutorial | 1 | 1.00 | 1.00 |
| Independent online learning hours | 20.00 | ||
| Private study hours | 54.00 | ||
| Total Contact hours | 26.00 | ||
| Total hours (100hr per 10 credits) | 100.00 | ||
Private study
- Independent learning to support ongoing formative tasks.- Substantive private study to prepare the student for the final examination.
Opportunities for Formative Feedback
Student progress will be monitored through on-going formative tasks to be implemented in parallel with lecture delivery, these will include self paced computer practical exercises and question and answer session within lectures.Methods of assessment
Coursework
| Assessment type | Notes | % of formal assessment |
| Report | 2,500 word Laboratory Report | 100.00 |
| Total percentage (Assessment Coursework) | 100.00 | |
A resit for this assessment will take place during the August Exam period.
Reading list
The reading list is available from the Library websiteLast updated: 20/03/2018
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- Undergraduate module catalogue
- Taught Postgraduate module catalogue
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