Simon Fraser University
Engaging the World

Course Outlines

Spring 2026 - CMPT 419 BLS1

Special Topics in Artificial Intelligence (3)

Biomedical Image Computing

Class Number: 5472

Delivery Method: Blended

Overview

  • Course Times + Location:

    Jan 5 – Apr 10, 2026: Wed, 11:30 a.m.–12:20 p.m.
    Burnaby

    Jan 5 – Apr 10, 2026: TBA, TBA
    Burnaby

Description

CALENDAR DESCRIPTION:

Current topics in artificial intelligence depending on faculty and student interest.

COURSE DETAILS:

Overview

Prerequisite note: We highly recommend that students have completed MATH 152 and MATH 232 as some course topics require knowledge of integration and working with vectors and matrices.

This course introduces various techniques for the acquisition, processing, analysis, and visualization of biomedical images. The course focuses on reviewing classical techniques for biomedical image computing as well as discussing some of the most recent advances in the field. The students will apply the knowledge through hands-on exercises and projects.

Topics

  • Biomedical image acquisition: including magnetic resonance imaging, X-ray computed tomography, ultrasound, nuclear medicine
  • Biomedical image file formats: e.g. DICOM
  • Biomedical image reconstruction, digitization, restoration
  • Biomedical image processing: including spatial, frequency-domain, and morphological filtering
  • Modelling shape variability: including boundary and region representation, statistics- and physics-based models
  • Biomedical image segmentation: including clustering, deformable models, region-based and level-set approaches
  • Biomedical image registration: including spatial transformations, similarity metrics, image interpolation, and optimization
  • Biomedical image visualization: including scalar, vector, and tensor field visualization, surface and volume rendering
  • Machine and deep learning tools and methods.
  • Software tools for biomedical image computing

COURSE-LEVEL EDUCATIONAL GOALS:

Learning Incomes

Upon entry, every student is expected to be able to:

  • write code using some programming or scientific computing language (e.g. C/C++, Java, Python, MATLAB)
  • basic familiarity with version control and markup languages (so you are ready to use or learn to use GitHub and LaTex on Overleaf)
  • understand documentation for using and calling functions from external libraries
  • be able to follow instructions for installing software and performing other basic IT tasks
  • understand high-school level concept in physics, biology, and chemistry
  • understand high-school and first-year undergraduate level concepts in mathematics (e.g. calculus, probability, trigonometry, vectors and matrices)

Learning Outcomes

At the end of the course, students are expected to be able to:

  • explain the basics of how different medical imaging modalities are acquired.
  • adopt biomedical image file formats in software developments.
  • implement and perform a variety of 2D and 3D biomedical image processing.
  • use mathematics and data structures to represent anatomical shapes and their variability.
  • develop and apply computational methods to delineate (segment) objects in medical images.
  • develop and apply computational methods to align (establish correspond between) a pair of medical images.
  • train a deep learning model to perform a biomedical image computing task
  • use existing programming libraries and software to display biomedical visual data.
  • leverage existing software tools for libraries to accelerate the development of biomedical image computing solutions.

Grading

NOTES:

Grading to be announced during the first week of classes.

Materials

RECOMMENDED READING:

Optional References and Resources:

  • Medical Image Analysis, Alejandro Frangi, Jerry Prince, Milan Sonka, 2023
  • Medical Image Analysis, Atam Dhawan, Wiley-IEEE Press, 2003
  • Insight into Images:  Principles and Practice for Segmentation, Registration, and Image Analysis, Terry Yoo, A K Peters Ltd., 2004
  • The ITK Software Guide, Ibanez et al, , :  Available online http://www.itk.org/ItkSoftwareGuide.pdf
  • Digital Image Processing Using MATLAB, Rafael C. Gonzalez, Richard E. Woods, Steven L. Eddins, Prentice Hall (4th Edition now available)
  • Digital Image Processing Using MATLAB, Rafael C. Gonzalez, Richard E. Woods, Steven L. Eddins, Prentice Hall, 2003
  • MATLAB Image Processing Toolbox User's Guide, , Mathworks, Available online from mathworks.com
  • Fundamentals of Digital Image Processing, Anil K. Jain, Prentice Hall, 1988
  • Image Processing:  Analysis and Machine Vision, Milan Sonka, Vaclav Hlavac, Roger Boyle, Thomson-Engineering (4th Edition now available)
  • Deformable Models in Medical Image Analysis, Ajit Singh, Dmitry Goldgof, Demetri Terzopoulos , IEEE, 1998
  • Active Contours, Andrew Blake, Michael Isard , Springer-Verlag, 1999
  • Handbook of Medical Imaging, Volume 2:  Medical Image Processing and Analysis , Milan Sonka, J. Michael Fitzpatrick , SPIE, 2000

REQUIRED READING NOTES:

Your personalized Course Material list, including digital and physical textbooks, are available through the SFU Bookstore website by simply entering your Computing ID at: shop.sfu.ca/course-materials/my-personalized-course-materials.

Department Undergraduate Notes:

The following are default policies in the School of Computing Science. Please check your course syllabus whether the instructor has chosen a different policy for your class, otherwise the following policies apply.
 
  • Students must attain an overall passing grade on the weighted average of exams in the course in order to get a C- or higher.
  • All student requests for accommodations for their religious practices must be made in writing by the end of the first week of classes, or no later than one week after a student adds a course. After considering a request, an instructor may provide a concession or may decline to do so. Students requiring accommodations as a result of a disability can contact the Centre for Accessible Learning (caladmin@sfu.ca).

Registrar Notes:

ACADEMIC INTEGRITY: YOUR WORK, YOUR SUCCESS

At SFU, you are expected to act honestly and responsibly in all your academic work. Cheating, plagiarism, or any other form of academic dishonesty harms your own learning, undermines the efforts of your classmates who pursue their studies honestly, and goes against the core values of the university.

To learn more about the academic disciplinary process and relevant academic supports, visit: 


RELIGIOUS ACCOMMODATION

Students with a faith background who may need accommodations during the term are encouraged to assess their needs as soon as possible and review the Multifaith religious accommodations website. The page outlines ways they begin working toward an accommodation and ensure solutions can be reached in a timely fashion.