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

A.A. 2024/2025 A.A. 2023/2024 A.A. 2022/2023 A.A. 2021/2022 A.A. 2020/2021 A.A. 2019/2020 A.A. 2018/2019 A.A. 2017/2018 A.A. 2016/2017

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 2025/2026

The 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.

The Study plan 2015/2016 will be available by May 2nd. While waiting for it to be published, consult the Study plan for the current academic year at the following link.

Legend | 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.

A Basic activities
B Characterizing activities
C Related or complementary activities
D Activities to be chosen by the student
E Final examination
F Other training activities
S Placements in companies, public or private institutions and professional associations

Biomedical image processing  (2016/2017)

Teaching code

4S004554

Academic staff

Alessandro Daducci, Gloria Menegaz

Coordinator

Alessandro Daducci

Credits

6

Language

English en

Scientific Disciplinary Sector (SSD)

INF/01 - INFORMATICS

Period

II sem. dal Mar 1, 2017 al Jun 9, 2017.

MoodleMoodle Seminars 0

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

Reference texts
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.

Students with disabilities or specific learning disorders (SLD), who intend to request the adaptation of the exam, must follow the instructions given HERE

Teaching materials e documents