Code | EB7 |
Type | Elective |
Semester | B |
ECTS credits | 5 |
COURSE OUTLINE
(1) GENERAL
SCHOOL | ENGINEERING | ||||
ACADEMIC UNIT | POSTGRADUATE PROGRAM in BIOMEDICAL ENGINEERING | ||||
LEVEL OF STUDIES | MSc | ||||
COURSE CODE | CEID_NE4828 | SEMESTER | Spring | ||
COURSE TITLE | MEDICAL IMAGE PROCESSING AND ANALYSIS | ||||
INDEPENDENT TEACHING ACTIVITIES if credits are awarded for separate components of the course, e.g. lectures, laboratory exercises, etc. If the credits are awarded for the whole of the course, give the weekly teaching hours and the total credits |
WEEKLY TEACHING HOURS | CREDITS | |||
Lectures and tutorials | 3 | 3 | |||
Laboratory Exercises | 2 | 2 | |||
Add rows if necessary. The organisation of teaching and the teaching methods used are described in detail at (d). | 5 | 5 | |||
COURSE TYPE
general background, |
Special background
skills development |
||||
PREREQUISITE COURSES:
|
Recommended prerequisite courses:
|
||||
LANGUAGE OF INSTRUCTION and EXAMINATIONS: | English (in case of foreign students attending the course). Also relevant material is available in English | ||||
IS THE COURSE OFFERED TO ERASMUS STUDENTS | YES | ||||
COURSE WEBSITE (URL) | https://eclass.upatras.gr/courses/CEID1033/ |
(2) LEARNING OUTCOMES
Learning outcomes | |
The course learning outcomes, specific knowledge, skills and competences of an appropriate level, which the students will acquire with the successful completion of the course are described.
Consult Appendix A
|
|
Α. Theory
The student that will successfully attend the course, will be able to:
B. Laboratory exercise The student that will successfully complete the laboratory part of the course, will be able to:
|
|
General Competences | |
Taking into consideration the general competences that the degree-holder must acquire (as these appear in the Diploma Supplement and appear below), at which of the following does the course aim? | |
Search for, analysis and synthesis of data and information, with the use of the necessary technology
Adapting to new situations Decision-making Working independently Team work Working in an international environment Working in an interdisciplinary environment Production of new research ideas |
Project planning and management
Respect for difference and multiculturalism Respect for the natural environment Showing social, professional and ethical responsibility and sensitivity to gender issues Criticism and self-criticism Production of free, creative and inductive thinking …… Others… ……. |
Working independently
Team work Working in an international environment Working in an interdisciplinary environment Production of new research ideas |
(3) SYLLABUS
A. Lectures
During the course, the following material, among others, will be covered:
- Introductory concepts for Image Processing & Analysis and their applications
- Basic elements of 2-D signal processing and image transforms
- Image acquisition systems and different types of degradation
- Image enhancement methods
- Image restoration methods
- Techniques for lossless and lossy image compression
- Elements of color theory and color image processing basics.
- Reconstruction of 3D objects based on 2D projections
- Edge detection and linking
- Image segmentation
- Shape description and representation
- Object recognition
- Basic structure of an image analysis system
- Basic principles of machine learning for image processing & analysis
- Elements of deep neural networks (DNN) theory and architectures
- Emphasis on DNN architectures suitable for image processing & analysis.
B. Laboratory exercises and project
Exercises:
- Exercise 1: Image transforms and image filtering in the frequency domain
- Exercise 2: Image quantization (scalar and vector)
- Exercise 3: Image compression using DCT transform
- Exercise 4: Histogram based image processing
- Exercise 5: Image restoration (inverse filtering, Wiener filtering)
- Exercise 6: Edge detection.
Project:
- Each student will choose to implement one from a list of possible projects. Indicative list of project subjects:
- Denoising of images (e.g. medical) based on sparse representation
- Registration of spatio-temporally correlated images
- Reconstruction of MRI images via dictionary learning techniques
- Comparative study of Convolutional Neural Networks for object classification (in different applications contexts)
- Image retrieval: Comparative study of techniques based on PCA and Autoencoders.
(4) TEACHING and LEARNING METHODS – EVALUATION
DELIVERY Face-to-face, Distance learning, etc. |
Face-to-face | ||||||||||||||||||||||
USE OF INFORMATION AND COMMUNICATIONS TECHNOLOGY Use of ICT in teaching, laboratory education, communication with students |
Extensive use of ICT tools. In particular:
|
||||||||||||||||||||||
TEACHING METHODS
The manner and methods of teaching are described in detail. Lectures, seminars, laboratory practice, fieldwork, study and analysis of bibliography, tutorials, placements, clinical practice, art workshop, interactive teaching, educational visits, project, essay writing, artistic creativity, etc.
The student’s study hours for each learning activity are given as well as the hours of non-directed study according to the principles of the ECTS |
|
||||||||||||||||||||||
STUDENT PERFORMANCE EVALUATION
Description of the evaluation procedure
Language of evaluation, methods of evaluation, summative or conclusive, multiple choice questionnaires, short-answer questions, open-ended questions, problem solving, written work, essay/report, oral examination, public presentation, laboratory work, clinical examination of patient, art interpretation, other
Specifically-defined evaluation criteria are given, and if and where they are accessible to students. |
Performance evaluation is based on:
|
(5) ATTACHED BIBLIOGRAPHY
– Suggested bibliography:
- «Digital Image Processing», R. Gonzalez and R. Woods, Pearson, 4th edition, 2017.
- «Digital Image Processing and Analysis», N. Papamarkos, Edition 2013 (in Greek)
– Related academic journals and conferences:
- IEEE Transactions on Image Processing
- IEEE Transactions on Signal Processing
- IEEE Signal Processing Magazine
- ELSEVIER – EURASIP Image Communication Journal
- IEEE ICIP, IEEE ICASP, IEEE Globalsip, Eusipco