Faculty of Technical Sciences

Subject: (17.EAI557)

General information:
 
Category Scientific-professional
Scientific or art field Telecommunications and Signal Processing
ECTS 6

Introduction to advanced methods of computerised analysis of medical images. Introduction to basic directions of machine vision development in medicine and application of acquired knowledge to solving realistic problems in medical image analsys.

Knowledge of contemporary digital image analysis tools used in quantitative and qualitative processing of diagnostic medical images. Understanding of the basic aims of various medical image analysis approaches, analysis contexts and differentiation of optimal application domains for various analysis algorithms and techniques. Practical experience in application of advanced methods of medical image analysis in real diagnostic imaging problems.

- Introduction to medical imaging – digital diagnostic medical imagery 2D/3D/4D, imaging modalities, resolution, isotropy, dynamic images, temporal resolution, interpolation - Medical image analysis concepts – analysis aims, diagnostic processing, quantitative analysis of signals beyond human perception, computer aided diagnosis - Multiscale image analysis – analysis and synthesis processes, pyramidal image representation, wavelets and Discrete Wavelet Transform - Image processing for presentation – multi-scale structure enhancement, dynamic range and MTF corrections, noise removal, tone scaling - Optimisation – numerical optimisation methods in medical image analysis, objective functions and distance measurement, hypothesis testing, global and local methods - Registration – establishment of geometric correspondence, image normalisation, (perspecte) image transformarions, deformations, deformable registration, deformation fields, objective measures - Segmentation – iluminatin segmentation, snakes, level sets, mean shift, graf cuts, Markov fields - Shape and appearance modeling – statistical shape and texture models, appearance models, active appearance models (AAM)

Oral lectures; active computer based exercises in solving realistic medical image analysis problems in Python/Matlab programming environment.

Authors Title Year Publisher Language
Toennies, Klaus D Guide to Medical Image Analysis 2017 Springer English
Le Lu, Yefeng Zheng, Gustavo Carneiro, Lin Yang Deep Learning and Convolutional Neural Networks for Medical Image Computing: Precision Medicine, High Performance and Large-Scale Datasets 2017 Springer English
V Lakshmanan, M Görner, R Gillard Practical Machine Learning for Computer Vision: End-to-End Machine Learning for Images 2021 O'Reilly Media English
Course activity Pre-examination Obligations Number of points
Project Yes Yes 30.00
Pismeni ispit No Yes 40.00
Computer excersise defence Yes Yes 30.00
API Image

Prof. Petrović Vladimir

Full Professor

Lectures

Assistant - Master Ninković Vukan

Assistant - Master

Laboratory classes

Faculty of Technical Sciences

© 2024. Faculty of Technical Sciences.

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© 2024. Faculty of Technical Sciences.