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Third-Cycle Courses

Faculty of Engineering | Lund University

Details for the Course Syllabus for Course FMAN30F valid from Autumn 2014

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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
Admission Requirements
Assumed Prior Knowledge
Selection Criteria
Literature
  • Material tillhandahålles av institutionen..
Further Information
Course code
  • FMAN30F
Administrative Information
  •  -12-19
  • FN1/Anders Gustafsson

All Published Course Occasions for the Course Syllabus

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