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# Details for Course FMAN30F Medical Image Analysis

General
• FMAN30F
• Temporary
Course Name
• Medical Image Analysis
Course Extent
• 7.5
Type of Instruction
• Course given jointly for second and third cycle
• 7151 (Centre of Mathematical Sciences / Mathematics)
•  -12-19
• FN1/Anders Gustafsson

## Current Established Course Syllabus

General
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.
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.
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.

Judgement and Approach
• For a passing grade the doctoral student must
Types of Instruction
• Lectures
• Laboratory exercises
• Exercises
Examination Formats
• Written assignments
• Failed, pass
Assumed Prior Knowledge
Selection Criteria
Literature
• Material tillhandahålles av institutionen..
Further Information
Course code
• FMAN30F
•  -12-19
• FN1/Anders Gustafsson

## All Established Course Syllabi

1 course syllabus.

Valid from First hand in Second hand in Established
Autumn 2014 2014‑12‑02 18:03:29 2014‑12‑03 10:47:39 2014‑12‑19

## Current or Upcoming Published Course Occasion

No matching course occasion was found.

## All Published Course Occasions

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