Course Syllabus for

Medical Image Analysis
Medicinsk bildanalys

FMAN30F, 7.5 credits

Valid from: Autumn 2014
Decided by: FN1/Anders Gustafsson
Date of establishment: 2014-12-19

General Information

Division: Mathematics
Course type: Course given jointly for second and third cycle
The course is also given at second-cycle level with course code: FMAN30
Teaching language: English


To prepare the postgraduate student for research on the border between medicin and engineering through a basic introduction to theory and mathematical methods used in medical image analysis.


Knowledge and Understanding

For a passing grade the doctoral student must

Competences and Skills

For a passing grade the doctoral student must

Course Contents

Basic concepts: Images, Volume data, 4D data, pixel, voxel, file-formats, DICOM (Digital Imaging and Communications in Medicine). Registration, segmentation, shape models, machine learning. Image acquisition techniques: Radiography, CT (X-ray computed tomography), MR (Magnetic resonance imaging), ultrasound, PET (Positron emission tomography), Scint (Scintigraphy) and SPECT (Single-photon emission computed tomography). 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.

Course Literature

Material tillhandahålles av institutionen..

Instruction Details

Types of instruction: Lectures, laboratory exercises, exercises

Examination Details

Examination format: Written assignments
Grading scale: Failed, pass

Admission Details

Assumed prior knowledge: FMAN20 Image Analysis or FMA170 Image Analysis

Course Occasion Information

Contact and Other Information

Course coordinators:
Web page:

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