Medical Image Analysis

Course description

Introduction: medical image analysis in clinical practice.

Image-guided medical procedures: intrinsic and extrinsic information-based tracking and navigation, procedure planning and visualization, registration of pre- and intra-interventional imaging data, patient models and treatment plans, validation of registration methods, applications of image-guided procedures.

 

Segmentation and quantitative analysis: classification and applicability of methods, thresholding, edge- and region-based techniques, model- and atlas-based methods, supervised and unsupervised methods, cluster-based, principal component analysis, statistical shape and appearance models.

Computer-aided diagnosis: feature selection and extraction, decision functions, distance measures in cluster analysis, statistical classification, fuzzy classification, neural networks, receiver operating characteristics (ROC), demonstration of successful applications.

Course is carried out on study programme

Elektrotehnika 2. stopnja

Objectives and competences

The students will obtain a theoretical and practical insight into the computational and mathematical methods in medical image processing and analysis. Student will be able to design  automated methods for image enhancement and registration, segmentation and quantification of medical images, such as X-ray, CT, MRI, PET, etc., in the context of automatic image-based diagnosis and monitoring, and image-guided medical interventions.  They will have the capacity to design, acquire and organize the imaging data and reference data and measurements and use this data to objectively and critically assess the quality of image analysis methods.

Learning and teaching methods

Theoretical foundations of automated methods and exemplar use cases are given during lectures, while practical skills are gained through lab works, weekly labwork assignements and individual seminar work.

Intended learning outcomes

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

  • list medical image formats, understand their structure and use the tools for interactive visualization and processing,
  • resolve, model, and analyse information contained in medical images,
  • apply this information in order to advance and enhance diagnosis, treatment and monitoring of diseases through engineering techniques,
  • design automated methods for image enhancement and registration, segmentation and quantification of medical images,
  • design, acquire and organize the imaging data and reference data and measurements and
  • use this data to objectively and critically assess the quality of image analysis methods.

Reference nosilca

  1. MITROVIĆ, Uroš, ŠPICLIN, Žiga, LIKAR, Boštjan, PERNUŠ, Franjo. 3D-2D registration of cerebral angiograms: a method and evaluation on clinical images. IEEE Tr Medical Imaging, 2013,  vol. 32, no. 8, str. 1550-1563.
  2. MITROVIĆ, Uroš, PERNUŠ, Franjo, LIKAR, Boštjan, ŠPICLIN, Žiga. Simultaneous 3D-2D image registration and C-arm calibration : application to endovascular image-guided interventions. Medical physics, 2015, vol. 42, no. 11, str. 6433-6448.
  3. GALIMZIANOVA, Alfiia, PERNUŠ, Franjo, LIKAR, Boštjan, ŠPICLIN, Žiga. Stratified mixture modeling for segmentation of white-matter lesions in brain MR images. NeuroImage, 2016, vol. 124, pt. A, str. 1031-104
  4. JERMAN, Tim, PERNUŠ, Franjo, LIKAR, Boštjan, ŠPICLIN, Žiga. Blob enhancement and visualization for improved intracranial aneurysm detection. IEEE Tr Visualization and Computer Graphics, 2016, vol. 22, no. 6, str. 1705-1717.
  5. LESJAK, Žiga, PERNUŠ, Franjo, LIKAR, Boštjan, ŠPICLIN, Žiga. Validation of white-matter lesion change detection methods on a novel publicly available MRI image database. Neuroinformatics, 2016, vol. 14, no. 4, str. 403-420.

Study materials

  1. Thomas M. Deserno. Biomedical Image Processing. Springer, 201
  2. Klaus D. Tonnies. Guide to Medical Image Analysis: Methods and Algorithms. Springer, 201
  3. Wolfgang Birkfellner. Applied Medical Image Processing, Second Edition: A Basic Course. CRC Press; 2 edition, 2014.
  4. Michael Fitzpatrick and Milan Sonka. Handbook of Medical Imaging, Volume 2. Medical Image Processing and Analysis (Parts 1 and 2) (SPIE Press Monograph Vol. PM80/SC), SPIE Publications; Reprint edition, 2009.
  5. Terry Peters, Kevin Cleary. Image-Guided Interventions: Technology and Applications, Springer, 1st edition, 2008.

Bodi na tekočem

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

E:  dekanat@fe.uni-lj.si T:  01 4768 411