Ghassan Hamarneh
 School of Computing Science
 Faculty of Applied Science
 Simon Fraser University

 ACM Senior Member & IEEE Senior Member
 BRC RAMP NeuroDevNet NeuroScience

Medical Image Analysis Lab

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CMPT 888: Medical Image Analysis (appears under: Special Topics in Computer Graphics, HCI, Computer Vision, and Visualization)

Next offering: TBD.
course outline

Course homepage

Note to non-SFU graduate students

  • General objectives of the course: Through round-table discussions and presentations of classical and state-of-the-art papers on medical image analysis, we will review and critique how different computational, engineering, and mathematical techniques are adapted and applied to the problem of extracting clinically useful information from medical images.
  • In this course you will:
    • learn about medical imaging modalities such as MRI, CT, and ultrasound, and recent advances such as functional and molecular imaging, diffusion tensor imaging, and time-varying medical images.
    • appreciate the need for developing automated, accurate, robust, and fast, techniques for extracting information from medical imaging for a variety of health applications.
    • learn how the field of medical image analysis involves understanding, adapting and applying a variety of computational, mathematical and engineering techniques which include: signal/image processing, optimization, AI, machine learning, graph theory, mathematical modeling, differential equations, multi-variate statistics,  geometrical modeling, matrix/tensor algebra, etc.
    • learn about classical and contemporary techniques for medical image segmentation, medical image registration, and shape analysis, which constitute three important sub-areas of medical image analysis.
    • learn what medical image segmentation is and why is it important for medicine, understand some of the most important established and researched techniques for segmentation, and appreciate some of the outstanding challenges
    • understand what medical image registration is,  its applications in health care,  the most established frameworks for registration, and what some of the remaining challenges are.
    • learn about different ways of representing and analyzing anatomical structures of deformable shapes and how can this translate into improved healthcare.
  • Project: The course involves working on a medical image analysis project. A wide range of projects related to the above topics is possible. The project topic can be either initiated by the student or recommended by the instructor. The project may  be very clinical application oriented or may deal with a theoretical aspect of  computational/mathematical techniques for medical image analysis, or a combination of both (see some example projects at our Medical Image Analysis Lab).
  • Advantages: there are many other advantages to studying topics at the interface between computing and medicine, including: wider career opportunities, graduate studies and funding, working with state-of-the-art software and hardware, collaborating with doctors and accessing valuable medical data... more are mentioned here.

vessel segmentation from 3D MR brain angiography (head seen from above, nose in lower right corner)

segmenting the spinal cord from a mid-sagittal MRI

segmentation of the rotator cuff muscle in the shoulder


Deformable registration of  Hoffa's fat pad between images of extended and flexed knees.


Registration of anatomical CT image with data from a functional SPECT scan


Manifold Learning and graph representation of medial (skeleton) based  shape descriptors.


Decomposing shape variability of a brain structure (corpus callosum)
into localized and intuitive deformation

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