Postgraduate Course: Neuroimaging: Image Analysis (NEME11035)
Course Outline
School | School of Clinical Sciences |
College | College of Medicine and Veterinary Medicine |
Course type | Online Distance Learning |
Availability | Not available to visiting students |
Credit level (Normal year taken) | SCQF Level 11 (Postgraduate) |
Credits | 10 |
Home subject area | Neuroscience (Medicine) |
Other subject area | None |
Course website |
None |
Taught in Gaelic? | No |
Course description | This elective 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)
Pre-requisites |
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Co-requisites | |
Prohibited Combinations | |
Other requirements | None |
Additional Costs | None |
Course Delivery Information
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Delivery period: 2013/14 Semester 2, Not available to visiting students (SS1)
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Learn enabled: Yes |
Quota: None |
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Web Timetable |
Web Timetable |
Course Start Date |
13/01/2014 |
Breakdown of Learning and Teaching activities (Further Info) |
Total Hours:
100
(
Lecture Hours 20,
Online Activities 20,
Formative Assessment Hours 2,
Summative Assessment Hours 2,
Revision Session Hours 20,
Directed Learning and Independent Learning Hours
36 )
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Additional Notes |
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Breakdown of Assessment Methods (Further Info) |
Written Exam
50 %,
Coursework
50 %,
Practical Exam
0 %
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No Exam Information |
Summary of Intended Learning Outcomes
An understanding of 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. On completion of the course, you 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. |
Assessment Information
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.
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Special Arrangements
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. |
Additional Information
Academic description |
Not entered |
Syllabus |
Not entered |
Transferable skills |
Not entered |
Reading list |
Not entered |
Study Abroad |
Not entered |
Study Pattern |
Not entered |
Keywords | Neuroimaging, Image Analysis, Radiology |
Contacts
Course organiser | Dr Andrew Farrall
Tel: (0131) 537 3910
Email: andrew.farrall@ed.ac.uk |
Course secretary | Mr Samuel Court
Tel: 0131 537 3125
Email: scourt2@exseed.ed.ac.uk |
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© Copyright 2013 The University of Edinburgh - 13 January 2014 4:46 am
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