FMAN30F

Temporary

Medical Image Analysis

7.5

Course given jointly for second and third cycle

7151 (Centre of Mathematical Sciences / Mathematics)

 -12-19

FN1/Anders Gustafsson

English

Every autumn semester

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.

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.

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

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.

Lectures

Laboratory exercises

Exercises

Written assignments

Failed, pass

FMAN20 Image Analysis or FMA170 Image Analysis

Material tillhandahålles av institutionen..

FMAN30F

 -12-19

FN1/Anders Gustafsson