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
Fundamentals of digital image: image formation, acquisition and digitalisation. Binary representation, storage and visualisation of digital images.
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.
Basic knowledge of programming and digital signal processing
Generic skills to reach
. Competence in analysis and synthesis; . Computer Skills for the scope of the study; . Competence to solve problems; . Competence in applying theoretical knowledge in practice; . Using the internet as a communication medium and information source; . Critical thinking; . Competence in autonomous learning; . Creativity; . Self-criticism and self-evaluation; (by decreasing order of importance)
Teaching hours per semester
total of teaching hours
Bibliography of reference
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
- 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 essy on a given theme).
Datashow (aulas teóricas e práticas). Computadores com IDL/MATLAB instalado (aulas práticas).