SylabUZ
| Course name | Medical image analysis algorithms |
| Course ID | 12.0-WF-FizD-MIAA-S17 |
| Faculty | Faculty of Physics and Astronomy |
| Field of study | Physics |
| Education profile | academic |
| Level of studies | Second-cycle studies leading to MS degree |
| Beginning semester | winter term 2019/2020 |
| Semester | 2 |
| ECTS credits to win | 7 |
| Available in specialities | Medical Physics |
| Course type | obligatory |
| Teaching language | english |
| Author of syllabus |
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| The class form | Hours per semester (full-time) | Hours per week (full-time) | Hours per semester (part-time) | Hours per week (part-time) | Form of assignment |
| Lecture | 30 | 2 | - | - | Exam |
| Laboratory | 30 | 2 | - | - | Credit with grade |
The aim of the course is to become familiar with basic image analysis algorithms as well as gaining practical skills in medical image analysis.
The ability to program with the use of the Python programming language. Basic signal analysis course as well as medical diagnostics and instrumentation course.
Medical image physics, instrumentation and acquisition
2d and 3d image formation, SNR, CNR (signal-to-noise, contrast-to-noise)
Image enhancement algorithms
Image feature detection
Elements of segmentation techniques
Backprojection algorithm and Radon theorem
Classification and clustering algorithms
Image quality and quality validation
Lectures and laboratory exercises, discussions, independent work with a specialized scientific literature in Polish and English, and work with the technical documentation and search for information on the Internet.
| Outcome description | Outcome symbols | Methods of verification | The class form |
Lecture: positive evaluation of the test.
Laboratory: positive evaluation of the tests, the execution of the project.
The final evaluation of the laboratory: evaluation of tests of 60%, the assessment of the project 40%.
Before taking the exam the student must be credited with the exercises.
Final grade: arithmetic mean of the completion of the lecture and in excerises.
[1] Klaus D. Toennies, Guide to Medical Image Analysis: Methods and Algorithms (Advances in Computer Vision and Pattern Recognition) 2012th Edition.
[2] Atam P. Dhawan, Medical Image Analysis 2nd Edition
[1] Kathy McQuillen Martensen, Radiographic Image Analysis, 4e 4th Edition.
Modified by dr hab. Piotr Lubiński, prof. UZ (last modification: 05-03-2020 15:32)
