Postgraduate Course: Neuroimaging: Image Analysis (NEME11035)
|Deanery of Clinical Sciences
|College of Medicine and Veterinary Medicine
|Credit level (Normal year taken)
|SCQF Level 11 (Postgraduate)
|Online Distance Learning
|Not available to visiting students
|This course is for students with a specific interest in Image Analysis, including those from a more computing background. Students will be able to focus in great detail on computing basics, sampling and quantisation as well as visual effects and their influence on perception; mathematical transformations and modelling as well as validation of techniques will also be taught so that on exit, students will independently be able to assess datasets from imaging experiments for quality, for best analysis approach including selecting the most appropriate analysis tools and algorithms, for sensibility and logic of output, and for usefulness and appropriateness to the original research goals.
Entry Requirements (not applicable to Visiting Students)
Course Delivery Information
|Academic year 2023/24, Not available to visiting students (SS1)
|Course Start Date
|Learning and Teaching activities (Further Info)
Lecture Hours 20,
Online Activities 20,
Formative Assessment Hours 2,
Summative Assessment Hours 2,
Revision Session Hours 20,
Directed Learning and Independent Learning Hours
|Assessment (Further Info)
|Additional Information (Assessment)
|Assessment will consist of continuous, in-course assessments and a final assessment. In-course assessment will be composed of a mixture of multiple choice-type questions, short essays, practical tasks, contributions to discussions and group learning activities - and will be delivered in time with individual modules making up the course. Final assessment will normally include a combination of multiple choice-type questions and short essays and it will take place at the end of the course period.
A number of bespoke technologies and the University's online assessment tool QuestionMark Perception will be used to deliver the more interactive and visually driven elements of assessment.
The principle of constructive alignment will underpin all assessments. This will ensure the assessment tasks are aligned with the specific course objectives.
|Besides summative feedback, formative feedback is provided throughout the course by tutors supporting the weekly course modules and also by the in-course assessment activity tutor.
|No Exam Information
On completion of this course, the student will be able to:
- Demonstrate knowledge and understanding of digital image basics, sampling and quantisation, influence of visual effects on perception, mathematical transformations & modelling, validation of image segmentation techniques.
- Independently assess datasets from imaging experiments for: quality, most appropriate analysis approach/tools/algorithms, usefulness & appropriateness to the original research goals.
- Manage own learning process to independently gain competence in effective use of prescribed software used for hands-on image analyses.
- Perform detailed analyses using specialised image analysis software, interpret the computed results and compare/contrast the analysis approaches used.
|Graduate Attributes and Skills
|All courses will be delivered taught by distance learning, using the institutional online learning environment and other online tools (e.g. wiki, objective testing software). These can be accessed by using the standard university EASE login.
|Prof Andrew Farrall
Tel: (0131) 537 3910
|Dr Charilaos Alexakis
Tel: 0131 537 3125