Checking date: 16/01/2024

Course: 2024/2025

Biomedical Image Processing
Master in Machine Learning for Health (Plan: 480 - Estudio: 359)

Coordinating teacher: DIAZ DE MARIA, FERNANDO

Department assigned to the subject: Signal and Communications Theory Department

Type: Electives
ECTS Credits: 6.0 ECTS


The objective of this course is that the student knows what a digital image is (sampling and quantification) and the basic image processing techniques (point-to-point operations, filtering, segmentation, morphological processing, etc). Additionally, it will deepen in the particular case of medical imaging, emphasizing on visualization techniques and multimodal image analysis and registration. On the other hand, students will not only study the techniques, but will also implement them in the laboratory, solving practical problems.
Skills and learning outcomes
Description of contents: programme
1.- Digital images. Sampling, quantization and color representation. 2.- Fundamentals of bioimages 3.- Basic image processing techniques 3.1. Pixel-wise transformations 3.2. Filtering 3.3. Fourier transform 3.4. Interpolation 3.5. Edge detection 3.6. Restoration 4.- Segmentation 5.- Morphological processing 6.- Feature extraction for image classification 7.- Multimodal image visualization and analysis 8.- Wavelets and multiresolution 9.- Keypoint detectors and descriptors 10.- Multidimensional image registration 11.- Advanced segmentation
Learning activities and methodology
AF3 Theoretical practical classes AF4 Laboratory practices AF6 Team work AF7 Student individual work AF8 Partial and final exams METODOLOGY MD1: Class lectures by the professor with the support of computer and audiovisual media, in which the main concepts of the course are developed and complemented with bibliography. MD3: Resolution of practical cases, problems, etc. .... posed by the teacher individually or in groups. MD4: Presentation and discussion in class, under the moderation of the professor, of topics related to the content of the course, as well as case studies. MD5: Elaboration of works and reports individually or in groups
Assessment System
  • % end-of-term-examination 0
  • % of continuous assessment (assigments, laboratory, practicals...) 100
Calendar of Continuous assessment
Basic Bibliography
  • G. Dougherty. Digital Image Processing for Medical Applications. Cambridge University Press. 2009
  • Mark A. Haidekker. Advanced Biomedical Image Analysis. John Willey and Sons. 2011
  • Rafael C. González and Richard E. Woods. Digital Image Processing. Fourth Edition, Pearson. 2018
Additional Bibliography
  • P. Suetens. Fundamentals of Medical Imaging. Cambridge University Press. 2009
  • Wilhelm Burger and Mark J. Burge. Principles of Digital Image Processing: Fundamental Techniques. Springer-Verlag. 2009
  • Wilhelm Burger and Mark J. Burge. Principles of Digital Image Processing: Core Techniques. Springer-Verlag. 2009

The course syllabus may change due academic events or other reasons.