Postgraduate Course: Practical Image Analysis 2 (NEME11055)
|School||Deanery of Clinical Sciences
||College||College of Medicine and Veterinary Medicine
|Credit level (Normal year taken)||SCQF Level 11 (Postgraduate)
|Course type||Online Distance Learning
||Availability||Not available to visiting students
|Summary||Advanced use & applications of MATLAB & toolboxes specific to image analysis & processing.
This practical, medical image analysis & processing course explores advanced MATLAB use & application. The course will help students assimilate & consolidate prior knowledge relating to image processing & analysis. Students will become familiar with 3D & 4D image operations, sophisticated image alignment & registration techniques, threshold-based image segmentation & classification, feature descriptors, machine learning applied to image segmentation & classification, 4D medical image analysis & processing, plus basic analyses of time series of volumetric image data.
Entry Requirements (not applicable to Visiting Students)
||Other requirements|| None
Course Delivery Information
|Academic year 2017/18, Not available to visiting students (SS1)
|Course Start Date
|Learning and Teaching activities (Further Info)
Lecture Hours 20,
Online Activities 20,
Formative Assessment Hours 4,
Revision Session Hours 20,
Programme Level Learning and Teaching Hours 2,
Directed Learning and Independent Learning Hours
|Assessment (Further Info)
|Additional Information (Assessment)
||Written Exam 0 %, Coursework 100 %, Practical Exam 0 %
Assessment will consist of continuous, in-course assessments composed of a mixture of multiple choice-type questions, practical tasks, contributions to discussions and group learning activities - and will be delivered in time with individual modules making up the course.
||Discussion boards will enable student queries to be addressed by course tutors.
Feedback on any Multiple choice questions delivered will be instant as it will be embedded in the software which delivers these assessments
|No Exam Information
On completion of this course, the student will be able to:
- Develop a critical understanding of advanced principles of image analysis using the MATLAB platform
- Develop specialised skills using the MATLAB platform to solve a varied range of 3D & 4D image analysis tasks
- Appraise various machine learning architectures & algorithms used in image segmentation, clustering & classification
- Critically review the work of peers and communicate insights and feedback in a professional manner
- Take responsibility to further own independent learning and skills development as required by the nature of the field of scientific computing / programming for medical Image Analysis
|A list of required and recommended readings will be provided through the institutional online learning environment.|
|Graduate Attributes and Skills
||1. Skills and abilities in Research and Enquiry: students will be equipped to obtain, assess, analyse and evaluate imaging with a view to investigating diseases; students will become more adept and competent at diagnosis, using well designed, methodologically sound and practical imaging techniques.
2. Skills & abilities in Personal & Intellectual Autonomy: the online eLearning delivery format means students have to be self-motivated and self-directed in order to complete the coursework successfully. They will be directed to compulsory readings in the literature which will act as a springboard for further readings. Activities will be designed so that students have to work independently, before discussing and presenting their work to peers and tutors.
3. Skills and abilities in Communication: the success of online distance learning depends on interaction with peers and tutors. Activities will all have group components where students communicate with each other and learn to work together to achieve various goals. Discussion boards associated with each Module also will allow students to post questions as well as to answer questions of their peers. Students will develop good practice in communication and collaboration using modern online communication tools, including but not restricted to asynchronous discussion forums, wikis and other web 2.0 tools.
4. Skills and abilities in Personal Effectiveness: the nature of the online distance learning environment means that students have to develop effective time management skills, as well as self-discipline with regards to regular study. They also need to juggle time-limited interactions with peers and tutors on which they are assessed.
||Besides the standard UoE computer requirements for ODL, students will need to have access to sufficient computing power in order to run MATLAB. For system requirements please visit the relevant Mathworks Inc webpage.
|Keywords||machine learning,segmentation,registration,fMRI,DWI,DTI,parametric maps,MATLAB,programming
|Course organiser||Prof Andrew Farrall
Tel: (0131) 537 3910
|Course secretary||Dr Charilaos Alexakis
Tel: 0131 537 3125