Course syllabus

Medicinsk bildanalys Medical Image Analysis

FMAN30, 7,5 credits, A (Second Cycle)

Valid for: 2014/15
Decided by: Education Board B
Date of Decision: 2014-04-08

General Information

Elective for: BME4-sbh, BME4-br, C5, D4, E4-mt, F4, F4-bm, Pi4-bg, Pi4-biek
Language of instruction: The course will be given in English on demand

Aim

The main aim of the course is to give a basic introduction to theory and mathematical methods used in medical image analysis, to an extent that will allow medical image processing problems to be handled. In addition the aim is to help the student develop his or her ability in problem solving, both with or without a computer. Furthermore, the aim is to prepare the student for further studies and research in the border between medicin and engineering.

Learning outcomes

Knowledge and understanding
For a passing grade the student must

• be able to explain clearly, and to independently use, basic mathematical concepts in medical image analysis, in particular regarding registration, segmentation and classification.
• be able to describe and give an informal explanation of some of the different image acquisition techniques used in medical imaging, e.g. Röntgen, CT, MR, ultrasound, PET, Scint and SPECT.
• be able to describe and give an informal explanation of the mathematical theory behind some central medical image processing algorithms
• have an understanding of the statistical principles used in machine learning

Competences and skills
For a passing grade the student must

• in an engineering manner be able to use computer packages to solve problems in medical image analysis.
• be able to show good capability to independently identify problems which can be solved with methods from medical image analysis, and be able to choose an appropriate method.
• be able to independently apply basic methods in medical image processing to problems which are relevant in medical applications or research.
• with proper terminology, in a well structured way and with clear logic be able to explain the solution to a problem in medical image analysis.

Contents

Basic concepts: Image, Volume data, 4D data, pixel, voxel, file-formats, DICOM.

Image acquisition techniequs: Radiography, CT, MR, ultrasound, PET, Scint and SPECT.

Noise and Image enhancement, loss-less compression

Registration: Registration of medical images. Mutual information. Landmark based methods. Deformation models.

Segmentation: active contours in 2D, 3D and 4D, active appearance models. Graph-methods.

Machine Learning: Training, testing, generalization, hypothesis spaces.

Validation: Databases. Ethics.

Examination details

Assessment: Compulsory assignments. Approved results on these are enough to pass the course. To get a higher grade, a written and oral test is required.