Biomedical Imaging Msc, Biosciences, University of Kent

BioMedical Imaging MSc, Biosciences, University of Kent

MODULE KI-704: Medical image computing MODULE SPECIFICATION

Convenor: Alan Colchester Team members: Ali Hojjat and Karl-Heinz Hoehne

1. The title of the module Medical image computing

2. The Department which will be responsible for management of the module Biosciences

3. The start date of the module January 2007

4. The number of students expected to take the module 10

5. Modules to be withdrawn on the introduction of this proposed module and consultation with other relevant Departments and Faculties regarding the withdrawal None

6. The Level of the module (eg Certificate[C], Intermediate[I], Honours[H], or Postgraduate[M]) Postgraduate [M]

7. The number of credits the module represents 20 (10 ECTs)

8. Which term(s) the module is to be taught in (or other teaching pattern) Spring term

9. Pre-requisite and co-requisite modules Pre-requisite modules:  Digital image processing  Applied mathematics and statistics  Introduction to computer systems and programming

10. The programme of study to which the module contributes MSc in BioMedical Imaging 11. The intended subject specific learning outcomes and, as appropriate, their relationship to programme learning outcomes On completing the module, it is expected that students should: a. Demonstrate a deep understanding of the fundamentals of selected advanced medical image processing techniques used for segmentation, object detection, co-registration, and partial volume correction. (A2, B1, C8) b. Demonstrate a deep understanding of the principles of selected visualization methods and their application in biomedical imaging. (A6) c. Have a clear understanding of high level representation of 3D images. (A2) d. Have a critical understanding of the concept of partial volume effect and correction techniques. (A1) e. Understand the application of filtering in noise reduction and image enhancement for surgical planning. (B5, C2 ) f. Be competent in applying pattern recognition algorithms for the analysis of biomedical data. (A4, B5, C3, C5, C6) g. Be able to design and implement an image processing algorithm, experiment on medical images, and analyse the results. (B2, C2, C4, C5, C6) h. Be able to carry out a project in the module topics with collaboration of scientists and medical profession. (B3, D1, B6) i. Be able to discuss and present technical issues clearly with specialists and non-specialists. (C7, C8)

12. The intended generic learning outcomes, and, as appropriate, their relationship to programme learning outcomes The module will help students to: j. Extend their ability to make effective use of general IT facilities in a computing environment. (D3). k. Communicate effectively by oral, written and visual means. (D2) l. Perform independent and efficient time management. (D4) m. Act autonomously in planning and implementing tasks at a professional level. (D1, D7)

13. A synopsis of the curriculum This course focuses on the theory and application of medical image processing and pattern recognition techniques in segmentation, registration, image guided intervention, and visualisation. This will involve gaining familiarity with selected region based, edge based, hybrid and model based segmentation techniques plus feature analysis, pattern analysis, clustering, classification algorithms, and their application in segmentation and recognition of objects. It will also involves gaining knowledge in concepts of 2D and 3D object correspondence, image transformation techniques, and application of image segmentation, co-registration, object recognition and visualization in surgical planning (including planning of interventions (surgery and minimally invasive) and intra-operative guidance). A basic knowledge of partial volume error (PVE) and selected PVE correction algorithms used for different image modalities will be covered. This module also covers commonly used validation techniques in medical image analysis. The course has some mathematical content although the pre-requisite modules are designed to ensure that students are at the required level. The course will equip those new to medical image processing with background knowledge of high level image representation and advanced image processing algorithms designed for biomedical images.

Practicals Assignments and/or report on experiments.

14. Indicative Reading List  Dhawan, A, “Medical Image Analysis”, Wiley-IEEE Press, 2003  Gonzalez and Woods, “Digital Image Processing”, Addison-Wesley 1993  YooT.S., “Insight into Images: Principles and Practice for Segmentation, Registration, and Image Analysis”, A K Peters, 2004.  Sonka, Hlavac, Boyle . “Image Processing, Analysis and Machine Vision” 2nd ed., PWS Publishing, 1999.  Boyle and Thomas, “Computer Vision - A First Course” 2nd Edition, Blackwell Science 1995.  Low, A, “Introductory Computer Vision and Image Processing”, McGraw-Hill 1991

15. Learning and Teaching Methods, including the nature and number of contact hours and the total study hours which will be expected of students, and how these relate to achievement of the intended learning outcomes The course will be run as a three-week module with lectures, and student-centred work (example class and presentations).

Total contact hours: 45 hours (38 hours lecture and 5 hours example class) Total learning hours for students: 200 hours.

16. Assessment methods and how these relate to testing achievement of the intended learning outcomes Course assessment tools include project report, example class and a final exam. Learning outcomes (a to f) will be assessed by examination (70%) Learning outcomes (a-m) will be assessed by the report on project. (30%)

17. Implications for learning resources, including staff, library, IT and space The course requires access to a MATLAB toolkit including image processing toolbox and access to Knowledge Centre in the Research & Development Centre .

Other resources will be supplied either centrally or within Medical Image Computing group.