Biomedical Imaging Technologies

Course description

Acquisition of biomedical images:

digital photography and video cameras, optical techniques, microscopic techniques, X-ray imaging, computed tomography, magnetic resonance imaging, ultrasound, emerging imaging technologies – physical principles, acquisition technologies and geometries, implementations and characteristics of imaging devices, image artefacts and quality.

Restoration, reconstruction and calibration: modelling and estimation of noise, image smoothing and sharpening, statistical and adaptive filtering, reconstruction algorithms, calibration and restoration of intensities, geometric calibration.

Image registration and integration:

classification and applications of image registration methods, modelling geometrical transformations and deformations, matching of control points, similarity based registration , similarity measures and optimization methods, analysis and evaluation of registration methods, image integration examples.

Course is carried out on study programme

Elektrotehnika 2. stopnja

Objectives and competences

To introduce basic technologies for the acquisition of biomedical images and the procedures for their restoration, reconstruction, calibration and integration.

Learning and teaching methods

Basic theory, procedures and practical examples are considered at lectures, while practical knowledge is gained through problem-solving tasks at lab works.

Intended learning outcomes

After successful completion of the course, students should be able to:

  • explain physical backgrounds of biomedical imaging techniques
  • describe technological limitations and artefacts
  • interpret information content of biomedical images
  • process digital images with computer programs
  • execute calibration and integration of multimodal images
  • evaluate image quality

Reference nosilca

  1. Jurij Jemec, Franjo Pernuš, Boštjan Likar in Miran Bürmen. Push-broom hyperspectral image calibration and enhancement by 2D deconvolution with a variant response function estimate. Optics Express, 22(22):27655–27668, 2014.
  2. Tomaž Vrtovec, Franjo Pernuš in Boštjan Likar. Investigation of the reproducibility and reliability of sagittal vertebral inclination measurements from MR images of the spine. Computerized Medical Imaging and Graphics, 38(7):620-627, 2014.
  3. Tim Jerman, Franjo Pernuš, Boštjan Likar in Žiga Špiclin. Enhancement of vascular structures in 3D and 2D angiographic images. IEEE Transactions on Medical Imaging, 35(9):2107-2118, 2016.
  4. Peter Naglič, Franjo Pernuš, Boštjan Likar in Miran Bürmen. Adopting higher-order similarity relations for improved estimation of optical properties from subdiffusive reflectance. Optics Letters, 42(7):1357-1360, 2017.
  5. Peter Naglič, Franjo Pernuš, Boštjan Likar in Miran Bürmen. Lookup table-based sampling of the phase function for Monte Carlo simulations of light propagation in turbid media. Biomedical Optics Express, 8(3):1895-1910, 2017.

Study materials

  1. Boštjan Likar in Miran Bürmen, Izročki predavanj pri predmetu Biomedicinske slikovne tehnike, Elektronsko gradivo, 2017.
  2. Miran Bürmen, Navodila za laboratorijske vaje pri predmetu Biomedicinske slikovne tehnike, Elektronsko gradivo, 2017.
  3. Miran Bürmen, Uvod v programski jezik Python, Založba FE, 2016.
  4. Boštjan Likar, Biomedicinska slikovna informatika in diagnostika, Založba FE in FRI, 2008.
  5. Jerry L. Prince, Jonathan Links, Medical Imaging Signals and Systems, Prentice Hall, 2nd edition, 2014.

Bodi na tekočem

Univerza v Ljubljani, Fakulteta za elektrotehniko, Tržaška cesta 25, 1000 Ljubljana

E: T:  01 4768 411