Automatic Cartilage Segmentation in Magnetic Resonance Imaging (2010–2013)

The ability to accurately and automatically obtain quantitative information from Magnetic Resonance images of the joint would improve clinical study efficiencies, as well as allow wider clinical use of more advanced and proven diagnosis techniques using Magnetic Resonance biochemical imaging. Our goals are to develop innovative automatic image processing methods including segmentation, analysis and fusion technologies to improve the delineation joint structures (e.g. bone , cartilage) to provide global and localized morphological and biochemical information for clinical use and studies. Once segmented the shape and size of cartilage and bones in a joint can then be used for Diagnoses or treatment planning and provide a valuable new tool.
Grant type:
ARC Linkage Projects
  • Associate Dean (Research)
    Faculty of Engineering, Architecture and Information Technology
    Affiliated Professor
    Centre for Advanced Imaging
  • Associate Professor
    School of Human Movement and Nutrition Sciences
    Faculty of Health and Behavioural Sciences
  • Honorary Professor
    School of Information Technology and Electrical Engineering
    Faculty of Engineering, Architecture and Information Technology
Funded by:
Australian Research Council