Biomedical image processing (2016/2017)

Codice insegnamento
4S004554
Docenti
Alessandro Daducci, Gloria Menegaz
Coordinatore
Alessandro Daducci
crediti
6
Settore disciplinare
INF/01 - INFORMATICA
Lingua di erogazione
Inglese
Periodo
II sem. dal 1-mar-2017 al 9-giu-2017.

Orario lezioni

II sem.
Giorno Ora Tipo Luogo Note
lunedì 14.30 - 16.30 lezione Aula L  
lunedì 16.30 - 19.30 laboratorio Laboratorio didattico Alfa  
martedì 13.30 - 15.30 lezione Aula Gino Tessari dal 7-mar-2017  al 9-giu-2017

Obiettivi formativi

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.

Programma

(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

Testi di riferimento
Autore Titolo Casa editrice Anno ISBN Note
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

Modalità d'esame

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.

Materiale didattico

Documenti

Opinione studenti frequentanti - 2015/2016


Statistiche per i requisiti di trasparenza (Attuazione Art. 2 del D.M. 31/10/2007, n. 544)

I dati relativi all'AA 2016/2017 non sono ancora disponibili