Mathematical Methods for Medical Image Analysis (07w5115)

Arriving in Banff, Alberta Sunday, November 4 and departing Friday November 9, 2007


(University of British Columbia)

(Medical Image Analysis Lab, Simon Fraser University)


* Bring together top-notch international researchers in the field of medical image computing for brainstorming sessions culminating by definition of potential research topics

* Foster exchange of information and insights among participants and seed new collaborations in areas of mutual interest

* Establish connections between Canadian researchers and students and well established research groups world wide

* Present exciting talks by the invited researchers on important problems in medical image analysis and the state of the art techniques that are currently under active research

* Provide a venue for students and faculty to learn about mathematical methods for medical imaging and applications (e.g. tutorial lectures)

* Improve communication between disciplines (Math, Computer Science, Engineering, Medicine, Physics) and make state-of-the-art methodology as accessible as possible to clinicians and bioscientists


The workshop will provide a forum for in-depth discussions and stimulating
interactions among high calibre researchers developing and applying
mathematical methods for solving problems in medical imaging. The workshop
will foster multidisciplinary research in medical image computing by
bringing together mathematicians, computer scientists, engineers,
physicists and clinicians. The focus will be on research topics in image
reconstruction, medical image processing and analysis (including filtering
and feature extraction, clustering and classification, model-based
segmentation and object recognition, tracking and motion analysis,
inter/intra-patient and inter/intra-modality registration), and
representation and rendering of complex visual medical datasets (including
geometrical and statistical deformable shape modeling,
visualization/display, and analysis).

The development of algorithms for solving these problems using medical
image datasets (scalar, vector, and tensor fields) exploits mathematical
tools including transforms, spectral analysis, PDEs, nonlinear
multivariate statistics, solutions to inverse problems, and optimization
methods. Applications of mathematical methods for medical imaging in
clinical and biomedical settings include computer aided-diagnosis and
intervention, therapy evaluation, computational anatomy, monitoring and
quantification of disease progression, and many others. The medical image
modalities of interest include magnetic resonance, ultrasound, X-ray
computed tomography, nuclear medicine, endoscopy, microscopy.