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DRPS : Course Catalogue : Deanery of Clinical Sciences : Neuroscience (Medicine)

Postgraduate Course: Practical Image Analysis 1 (NEME11054)

Course Outline
SchoolDeanery of Clinical Sciences CollegeCollege of Medicine and Veterinary Medicine
Credit level (Normal year taken)SCQF Level 11 (Postgraduate)
Course typeOnline Distance Learning AvailabilityNot available to visiting students
SCQF Credits10 ECTS Credits5
SummaryIntroduction to the use & applications of MATLAB & toolboxes specific to image analysis & processing.
Course description This practical, medical image analysis & processing course introduces MATLAB, an industry-standard operational platform for computational image analysis. Students will also work with related software, which interacts with MATLAB, & become familiar with 2D & 3D image operations, various medical image file formats, image enhancement, image alignment, & registration. Armed with this knowledge, students will tackle different medical image processing & analysis tasks, organised by topic & increasing difficulty as the course progresses.
Entry Requirements (not applicable to Visiting Students)
Pre-requisites Co-requisites
Prohibited Combinations Other requirements None
Course Delivery Information
Academic year 2017/18, Not available to visiting students (SS1) Quota:  None
Course Start Semester 1
Course Start Date 18/09/2017
Timetable Timetable
Learning and Teaching activities (Further Info) Total Hours: 100 ( 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 34 )
Assessment (Further Info) Written Exam 0 %, Coursework 100 %, Practical Exam 0 %
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.
Feedback 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
Learning Outcomes
On completion of this course, the student will be able to:
  1. Develop a critical understanding of the principles of image analysis using the MATLAB platform (including authoring scripts, functions and pipelines for concatenating different stages of medical image processing and analysis).
  2. Develop specialised skills using the MATLAB platform to solve a varied range of 2D & 3D image analysis tasks
  3. Gain critical insight into challenges underpinning image analysis, including image enhancement and compensating for artefacts
  4. Develop specialised skills to critically interpret medical images and to communicate the findings to a range of audiences.
  5. 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
Reading List
A list of required and recommended readings will be provided through the institutional online learning environment.
Additional Information
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.
Special Arrangements 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.
Keywordsmachine learning,classification,thresholding,segmentation,registration,MATLAB,programming
Course organiserProf Andrew Farrall
Tel: (0131) 537 3910
Course secretaryDr Charilaos Alexakis
Tel: 0131 537 3125
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