Advanced Topics in Medical Image

Universidad Carlos III de Madrid

Course Description

  • Course Name

    Advanced Topics in Medical Image

  • Host University

    Universidad Carlos III de Madrid

  • Location

    Madrid, Spain

  • Area of Study

    Biomedical Engineering, Biomedical Sciences

  • Language Level

    Taught In English

  • Prerequisites

    STUDENTS ARE EXPECTED TO HAVE COMPLETED:

    Signals and Systems
    Image processing and reconstruction

  • Course Level Recommendations

    Upper

    ISA offers course level recommendations in an effort to facilitate the determination of course levels by credential evaluators.We advice each institution to have their own credentials evaluator make the final decision regrading course levels.

    Hours & Credits

  • ECTS Credits

    6
  • Recommended U.S. Semester Credits
    3
  • Recommended U.S. Quarter Units
    4
  • Overview

    Advanced topics in medical image (257 - 15562)
    Study: Bachelor in Biomedical Engineering
    Semester 2/Spring Semester
    4th Year Course/Upper Division

    Students are Expected to have completed:

    Signals and Systems
    Image processing and reconstruction

    Compentences and Skills that will be Acquired and Learning Results:

    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: Course Description

    1. Review of basic concepts in image processing using ImageJ as a learning tool.
    2. Information Systems in the Medical Environment: DICOM, RIS and PACS.
    3. Image registration
    4. Adaptive filters
    5. Detecting lines and shapes: Hough transform
    6. Feature extraction and Statistical Classification
    7. Snakes and Active contours
    8. Wavelets and multiresolution processing. Super-resolution and blind deconvolution
    9. Tomographic Image Reconstruction in Projective Systems
    10. 3D and 4D image visualization. Software tools for medical image analysis.

    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:

    Continuous evaluation
    It accounts for up to 40% of the final score of the subject, and includes three components:
    1) Short-exams: These exams will take place mostly during seminars, and will be announced at least one week in advance.
    2) Practical sessions and homework exercises: They will be assessed through quizzes or exercises to be solved in groups or individually or a laboratory notebook or report in that will be handed in at the end of each practical session. Attendance to at least 80% of the practical sessions is mandatory; otherwise the score will be 0 in this item.
    3) Student participation: It includes contribution to seminars, forum in Aula Global, attitude, or other activities.
    4) Students can present a Final Project that may reduce the contribution of the final exam to the total grade.

    Final exam
    The final exam will cover the whole subject and will account 60 % of the final score. The minimum score in the final exam to pass the subject is 4.0 over 10, notwithstanding the mark obtained in continuous evaluation.

    Extraordinary exams
    The mark for students attending any extraordinary examination will be the maximum between:
    a) 100% extraordinary exam mark, or
    b) 60% extraordinary exam mark and 40% continuous evaluation if it is available in the same course.

    Academic conduct
    All exams will be closed-book, closed-notes, no PC or mobile phone, or anything else other than a writing implement and the exam itself. Plagiarism, cheating or other acts of academic dishonesty will not be tolerated. Any infractions whatsoever will result in a failing grade.

    Basic Bibliography:

    G. Dougherty. Digital Image Processing for Medical Applications. Cambridge Univ Press. 2009. ISBN-13: 978-0521860857
    Milan Sonka, Vaclav Hlavac, Roger Boyle. Image Processing, Analysis and Machine Vision. . Nelson Engineering. 3rd edition. . 2007. ISBN-13: 978-0495244387
    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

Course Disclaimer

Courses and course hours of instruction are subject to change.

ECTS (European Credit Transfer and Accumulation System) credits are converted to semester credits/quarter units differently among U.S. universities. Students should confirm the conversion scale used at their home university when determining credit transfer.