NDAK10005U Medical Image Analysis
Volume 2013/2014
Content
Medical
diagnosis, prognosis and quantification of progression is in
general based on biomarkers. These may be blood or urin markers,
but currently imaging is taking over as more indicative for many
purposes.
This course will give an introduction to medical image formation in the different scanning modalities: X-ray, CT, MR, fMRI, PET, US etc. We will continue with the underlying image analysis disciplines of detection, registration, and segmentation, and end with specific applications in clinical practise. A key to achieve success in the application is formal evaluation of methodologies why performance characterisation also is a central topic.
We will use techniques from image analysis and real world examples from the clinic.
The course is aimed at providing sufficient background knowledge for doing master theses (specialer) as well as student projects.
The course will cover essential aspects of medical image analysis. Among the topics are:
This course will give an introduction to medical image formation in the different scanning modalities: X-ray, CT, MR, fMRI, PET, US etc. We will continue with the underlying image analysis disciplines of detection, registration, and segmentation, and end with specific applications in clinical practise. A key to achieve success in the application is formal evaluation of methodologies why performance characterisation also is a central topic.
We will use techniques from image analysis and real world examples from the clinic.
The course is aimed at providing sufficient background knowledge for doing master theses (specialer) as well as student projects.
The course will cover essential aspects of medical image analysis. Among the topics are:
- Physics of X-ray formation
- Computed tomography
- Magnetic Resonance Imaging
- Functional MRI
- Positron Emission Tomography
- Single Photon Emission Tomography
- Medical statistics
- Segmentation by Watersheds
- Pixel classification
- Shape modelling
- Rigid registration
- Non-rigid registration
- Multi-model registration
- Shape statistics
- Applications in Lung diseases
- Application in cardiovascular diseases
- Applications in joint diseases
- Applications in neurology
Learning Outcome
Knowledge, skills, and
competences.
Literature
See Absalon when the course
is set up.
Academic qualifications
The students are expected
to have a mature and operational mathematical knowledge. Linear
algebra, geometry, basic mathematical analysis, and basic
statistics are mandatory disciplines.
Teaching and learning methods
Lectures, exersises, and
assignments
Workload
- Category
- Hours
- Class Instruction
- 206
- Total
- 206
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As an exchange, guest and credit student - click here!
Continuing Education - click here!
Exam
- Credit
- 7,5 ECTS
- Type of assessment
- Continuous assessmentContinuous assessment (4-7 written homework assignments). Grades given according to the 7-step scale. Internal grading.
- Marking scale
- 7-point grading scale
- Censorship form
- No external censorship
- Re-exam
- Oral exam (25 minutes without preparation). Internal grading and grades given according to the 7-step scale.
Criteria for exam assesment
See learning outcome.
Course information
- Language
- English
- Course code
- NDAK10005U
- Credit
- 7,5 ECTS
- Level
- Full Degree Master
- Duration
- 1 block
- Placement
- Block 2
- Schedule
- B
- Continuing and further education
- Study board
- Study Board of Mathematics and Computer Science
Contracting department
- Department of Computer Science
Course responsibles
- Sune Darkner (darkner@di.ku.dk)
Saved on the
09-07-2013