Studying at the University of Verona
Here you can find information on the organisational aspects of the Programme, lecture timetables, learning activities and useful contact details for your time at the University, from enrolment to graduation.
Study Plan
This information is intended exclusively for students already enrolled in this course.If you are a new student interested in enrolling, you can find information about the course of study on the course page:
Laurea magistrale in Medical bioinformatics - Enrollment from 2025/2026The Study Plan includes all modules, teaching and learning activities that each student will need to undertake during their time at the University.
Please select your Study Plan based on your enrollment year.
1° Year
| Modules | Credits | TAF | SSD |
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A scelta un insegnamento traA scelta due insegnamenti tra2° Year activated in the A.Y. 2017/2018
| Modules | Credits | TAF | SSD |
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A scelta tre insegnamenti tra| Modules | Credits | TAF | SSD |
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A scelta un insegnamento traA scelta due insegnamenti tra| Modules | Credits | TAF | SSD |
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A scelta tre insegnamenti traLegend | Type of training activity (TTA)
TAF (Type of Educational Activity) All courses and activities are classified into different types of educational activities, indicated by a letter.
Biomedical image processing (2017/2018)
Teaching code
4S004554
Academic staff
Coordinator
Credits
6
Language
English
Scientific Disciplinary Sector (SSD)
INF/01 - INFORMATICS
Period
II sem. dal Mar 1, 2018 al Jun 15, 2018.
Location
VERONA
Learning outcomes
The course deals with the major sources of medical imaging data (X-rays, CT, MRI, PET and US) and provides the students with a flavour of the current methods used to process medical images, enhance their quality and extract useful information from them. A particular focus will be given to diffusion MRI, as it represents a very rich imaging modality that will allow us to investigate several analysis techniques starting from the same data, at increasing levels of complexity.
These concepts are also illustrated in hands-on sessions where these techniques are applied to practical situations and problems that often arise when analyzing real medical images. The laboratory activities will be based on the Python language.
Program
(1) Basic concepts
- Image properties: pixel vs voxel, spatial resolution, orientation, data type etc
- File formats: DICOM, NIFTI, MINC etc
- Signal-to-noise (SNR) vs Contrast-to-noise (CNR) ratio
- Noise, blurring and modality-specific artifacts
- Signal representation: frequency domain, spherical harmonics, sparse bases
(2) Overview of major medical imaging modalities
- Radiography: X-rays projection, fluoroscopy and computed tomography (CT)
- Nuclear medicine: SPECT and PET
- Ultrasound imaging (US)
- Magnetic Resonance Imaging (MRI)
(3) Basic image processing
- Recall of elementary tools: filtering, edge detection and image enhancement
- Registration: features, similarity measures, transformations (linear vs non-linear)
(4) Connectivity analysis with diffusion MRI
- Principles and main applications
- Local reconstruction: DTI, DSI, CSD etc
- Tissue microstructure estimation: axon diameter mapping, AxCaliber, ActiveAx, CHARMED, NODDI etc
- Tractography: local vs global methods, probabilistic, recent advances
(5) Laboratory
- Introduction to Python
- Hands-on activities on the topics covered throughout the course
| Author | Title | Publishing house | Year | ISBN | Notes |
|---|---|---|---|---|---|
| Ravishankar Chityala | Image processing and acquisition using Python (Edizione 1) | Chapman and Hall/CRC | 2014 | 9781466583757 | |
| Andrew Webb | Introduction to biomedical imaging | Wiley-IEEE Press | 2003 | 978-0-471-23766-2 | |
| Jerrold T. Bushberg | The Essential Physics of Medical Imaging (Edizione 3) | Lippincott Williams & Wilkins | 2011 | 0781780578 |
Examination Methods
The grade will be based on a discussion about the final project assigned during the course. The final project is a very important part of the course, as it allows students to synthesize the concepts learned throughout the course, understand the motivation behind each modality, experiment typical problems that arise in daily-life medical images and apply the appropriate techniques to improve image quality and extract useful information.
