Checking date: 29/04/2019


Course: 2019/2020

Advanced topics in medical image
(15562)
Study: Bachelor in Biomedical Engineering (257)


Coordinating teacher: PASCAU GONZALEZ GARZON, JAVIER

Department assigned to the subject: Department of Bioengineering and Aerospace Engineering

Type: Electives
ECTS Credits: 6.0 ECTS

Course:
Semester:




Students are expected to have completed
Signals and Systems Image processing and reconstruction
Competences and skills that will be acquired and learning results. Further information on this link
The goal of this course is to provide the students with a complete understanding of advanced techniques for image processing in the field of medical imaging. Using the concepts already learn in Image Processing, the student will be able to process images with techniques such as automatic segmentation, machine learning methods or pattern recognition. Image reconstruction from acquired data in CT, MR and Nuclear Medicine will also be covered. After completion of the course, the student should be able to select the proper approach to process medical image data depending on the modality and the desired output, to write the necessary program and to evaluate the results.
Description of contents: programme
1. Review of basic concepts in image processing 2. Information Systems in the Medical Environment: DICOM, RIS and PACS. 3. 3D and 4D image visualization. Software tools for medical image analysis. 4. Wavelets and multi-resolution processing. 5. Advanced Segmentation I: detecting lines and shapes with Hough transform and active contours. 6. Image registration. 7. Advanced Segmentation II:Adaptive filters. 8. Image processing for feature extraction. 9. Dimensionality reduction and machine learning methods. 10. Tomographic Image Reconstruction in Projective Systems 11. Beyond classical methods: deep learning and convolutional networks.
Learning activities and methodology
Teaching methodology will be mainly based on lectures, seminars and practical sessions. Students are required to read assigned documentation before lectures and seminars. Lectures will be used by the teachers to stress and clarify some difficult or interesting points from the corresponding lesson, previously prepared by the student. Seminars will be mainly dedicated to interactive discussion with the students, present and evaluate homework. Grading will be based on continuous evaluation (including short-exams, homework, group essays, practical sessions, and student participation in class and Aula Global) and a final exam covering the whole subject. Help sessions and tutorial classes will be held prior to the final exam. Attendance to lectures, practical sessions, short-exams or submission of possible homework is not compulsory. However, failure to attend any exam or submit the exercises before the deadline will result in a mark of 0 in the corresponding continuous evaluation block. The practical sessions may consist on laboratory work or visits to research or clinical centers. A laboratory report will be required for each of them. Homework exercises will also be a very important contribution, since they will imply solving a specific problem, proposing an algorithm and implement it using computer tools. The attendance to 80% of practical sessions is mandatory. Failure to hand in the laboratory reports on time or unjustified lack of attendance will result in 0 marking for that continuous evaluation block. Some activities could reduce the total weight of the final exam, such as projects or open essays to be presented at the exam.
Assessment System
  • % end-of-term-examination 60
  • % of continuous assessment (assigments, laboratory, practicals...) 40
Basic Bibliography
  • Javier Pascau; José María Mateos Pérez. Image Processing with ImageJ. Packt Publishing. 2013
  • G. Dougherty. Digital Image Processing for Medical Applications. Cambridge Univ Press. 2009. ISBN-13: 978-0521860857
  • Mark A. Haidekker. Advanced Biomedical Image Analysis. John Willey and Sons. 2011. ISBN 978-0-470-62458-6
  • R. C. Gonzalez, R. E. Woods.. Digital Image Processing. Pearson Education. 3rd edition. . 2008. ISBN-13: 978-0135052679
Additional Bibliography
  • Isaac Bankman. Handbook of Medical Image Processing and Analysis. Academic Press Inc. 2nd Ed.. 2008. ISBN-13: 978-0123739049
  • Jiri Jan. Medical Image Processing, Reconstruction, and Restoration: Concepts and Methods. Taylor & Francis Ltd. 2005. ISBN-13: 978-0824758493
  • Terry S. Yoo.. Insight into Images: Principles and Practice for Segmentation, Registration, and Image Analysis. A K Peters. 2004. ISBN-13: 978-1568812175

The course syllabus and the academic weekly planning may change due academic events or other reasons.