OVERVIEW

The influence and impact of digital images on modern society is tremendous, and image processing is now a critical component in science and technology. The rapid progress in computerized medical image reconstruction, and the associated developments in analysis methods and computer-aided diagnosis, has propelled medical imaging int one of the most important sub-fields in scientific imaging. The course takes its motivations from medical applications and uses real medical images and situations to clarify concepts and to build intuition and understanding. Designed for students who will become end users of digital image processing, the course emphasizes the effective use of image processing tools

AIMS AND CONTENT

LEARNING OUTCOMES

The course aims to provide the tools for analyzing, understanding and extracting information from biomedical or biological images. During the course the characteristics of the different types of diagnostic imaging will be presented and students will develop small projects (with Matlab and with open source platforms) in working groups.

AIMS AND LEARNING OUTCOMES

This medical imaging course provides an introduction to biomedical imaging and modern imaging modalities. The course also covers the basic scientific principals behind each modality, and introduces some of the key applications, from neurological diseases to cancers (from micro to macro). This course includes modules specially designed for the students, whilst also providing some advanced modules which could contribute to professional development in health, engineering and IT industries.
Attendance and active participation in the proposed training activities (lectures and group projects) and individual study will allow the student to:
- to know  the components defining image quality, and basic image operations used to process digital images;
- to know  the most important imaging modalities today are discussed: radiography, computed tomography, magnetic resonance imaging, nuclear medicine imaging, and ultrasonic imaging.
- to know the theory for morphlological image processing, image segmentation, features recognition and classification, three-dimensional visualization

PREREQUISITES

It is assumed that the student knows the differential equations, has a good physical-chemical basis, has followed a course in electrical and electronic circuits, an introductory course in physiology or modeling of physiological systems and a course of programming (in particular Matlab)

TEACHING METHODS

 The teaching consists in combination of ltraditional lectures (24 hours), and a part of practice at the computer activities (45 hours - work group). Required course attendance as per the Didactic Regulations. The laboratory will be held by the teaching teachers, assisted by laboratory tutors.

SYLLABUS/CONTENT

The course program includes the presentation and discussion of the following topics:

• Imaging system
• The quality of a digital image
• Color images
• Medical imaging modalities
• The gray-level histogram
• Histogram transformations and look-uo tables
• Algebraic operations
• Geometric operations
• Convolution-based operations
• The Fourier domain and application in bioimaging
• Tomographic reconstruction
• Image degradation
• Noise and noise-reduction filters
• Morphological operations
• Image segmentation
• Feature recognition and classification
• Three-dimensional visualization
• Medical applications of imaging
• Frontiers of image processing in medicine

RECOMMENDED READING/BIBLIOGRAPHY

All slides used during lessons and other teaching materials will be available on aula@web. The books listed below are strongly suggested as supporting texts to complete the student's preparation, but students can also use other university level texts as long as they are published in the last 5 years.

1. J.G. Webster. Medical Instrumentation - Application and Design 4th Edition. Wiley.
2. Rafael C. Gonzales, Richard E. Woods - Elaborazione delle immagini digitali
3. Zhi - Pei Liang, Paul C. Lauterbur - Principles of Magnetic Resonance Imaging
4. Scott A. Huettel, Allen W. Song - Functional Magnetic Resonance Imaging
5. Geoff Dougherty - Digital image processing for medical applications
6. Paul Suetens - Foudamentals of medical imaging
7. Nadine Barrie Smith and Andrew Webb - Introduction to biomedical imaging

TEACHERS AND EXAM BOARD

LESSONS

LESSONS START

https://corsi.unige.it/corsi/11159

Class schedule

The timetable for this course is available here: Portale EasyAcademy

EXAMS

EXAM DESCRIPTION

Exam consists of a written/oral test and imaging elaboration  projects.

ASSESSMENT METHODS

The final examination will consist an written/oral test, which may include closed-loop tests, algebraic or numeric exercises, questions related to theoretical aspects, and imaging elaboration  projects.

FURTHER INFORMATION

Teacher office: Lab. Neuroengineering &System Neuroscience  - Via Dell'Opera Pia 13 - Pad E First Floor