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

Aim

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.

Goals

Knowledge and Understanding

For a passing grade the doctoral 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. Radiography, CT (X-ray computed tomography), MR (Magnetic resonance imaging), ultrasound, PET (Positron emission tomography), Scint (Scintigraphy) and SPECT (Single-photon emission computed tomography).
• 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 doctoral 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.

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 tillhandahÃ¥lles av institutionen..

Instruction Details

Types of instruction: Lectures, laboratory exercises, exercises

Examination Details

Examination format: Written assignments