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

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.

Basic knowledge of programming and digital signal processing