Image Processing and Analysis

Year
0
Academic year
2020-2021
Code
02003545
Subject Area
Biomedical Engineering
Language of Instruction
Portuguese
Other Languages of Instruction
English
Mode of Delivery
Face-to-face
Duration
SEMESTRIAL
ECTS Credits
6.0
Type
Elective
Level
2nd Cycle Studies - Mestrado

Recommended Prerequisites

Basic knowledge of programming and digital signal processing.

Teaching Methods

- Oral presentation using audiovisual means
- Examples that explore additional sources such as the internet and latest research results
- Group discussion of practical problems
- Solving programming problems
- Frequent practical tests.
- Writing of an essay (either a programming project's report or an essay on a given theme).

Learning Outcomes

This course is planned so as to enable the students to:
1) understand the theoretical foundations of digital image processing, including their context in the acquisition and analysis of biomedical images, and learn some of the main techniques
2) develop skills allowing them to put in practice what they've learned, mastering the appropriate image processing tools and, in particular, a specialised programming language.

Work Placement(s)

No

Syllabus

Introduction.
Fundamentals of digital image: image formation, acquisition and digitalisation. Binary representation, storage and visualisation of digital images.
Image characterisation.
Spatial domain processing: histograms, equalisation, image improvement. Spatial filtering.
Spectral domain processing: Fourier transforms. Filters. FFT. Convolution  and correlation theorem.
Image recovery: degradation/recovery process model. Noise models. Deconvolution.
Colour processing: colour models.
Shape processing and segmentation: dilation, erosion.
Detection/extraction of characteristics. Hough transform. Domain growth.
Image reconstruction: data organization. Radon transform. Analytical and iterative methods. Reconstruction.
Other techniques: alignment and fusion. PCA. "Machine Learning".
Practical classes syllabus: use of programming languages for image processing and visualisation.

Assessment Methods

Continuous assessment
Project: 10.0%
Mini Tests: 20.0%
Frequency: 70.0%

Final assessment
Project: 10.0%
Exam: 90.0%

Bibliography

Livro de referência / main book:

R. C. Gonzalez and R. E. Woods, Digital Image Processing, Prentice Hall, 2nd ed., 2001

Outros livros / Other books:

Rangaraj M R, Biomedical Image Analysis, CRC Press, 2005

R. C. Gonzalez, R. E. Woods, S. L. Eddins, Digital Image Processing using Matlab, Prentice Hall, 2004

Anil J. Kain, Fundamentals of Digital Image Processing, Prentice Hall, 1989