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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Quick visual overview of MIA research

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  • The overarching objective of my research is to enable computers to extract clinically useful information from medical images to improve our understanding, prevention, diagnosis, and treatment of diseases.
  • I work on developing computational techniques for solving real-world clinical problems through the automated processing and analysis of multi-dimensional biomedical structural and functional images. Medical images include magnetic resonance imaging (MRI, f-MRI, d-MRI) and computed tomography (X ray-CT, PET, SPECT), microscopy, and ultrasound.
  • I am interested in the designing, adapting and applying machine learning, optimization, graph theory, and other computational techniques to medical imaging and clinical applications.
  • My research focuses on developing techniques for: image classification, image segmentation and registration, tracking and matching, shape representation and deformation analysis of anatomical structures and functional regions in medical images. I also work on building statistical, physical, and geometrical models of anatomical shape variation and on their application to automated detection of structural abnormality and pathology through shape and image classification.
  • More recently, my research group is focusing on the development of hybrid engineered and machine learning (and deep learning) based methods applied to medical imaging and clinical meta-data for studying cancer, its diagnosis and treatment.
Research Components
  • Clinical Applications (e.g. cardiology, neurology,  oncology, and musculoskeletal)
  • Medical Imaging Modalities (e.g. MRI, CT, ultrasound, nuclear, microscopy/nanoscopy,  2D, 3D, 3D+time, scalar, vector, tensor fields)
  • Image processing (e.g. image filtering, enhancement, noise reduction, edge detection)
  • Image segmentation (e.g. identifying anatomical structures of interest in medical images for quantification, visualization)
  • Image Registration and Surface Matching (e.g. fusion of medical images of different modalities, establishing correspondence between images and shapes)
  • Image Classification (e.g. computer assisted diagnosis)
  • Statistical Shape Analysis (e.g. 3D shape representation, modeling anatomical shape variability, detecting shape abnormalities)
  • Software Tools (see examples)
  • medical image analysis
  • image segmentation, image registration, shape analysis,
  • deformable models,  geometric modeling,
  • physics-based shape modeling, statistical shape modeling,
  • artificial life
  • medical imaging
  • small animal image analysis
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