Distributed multi-modal image collection and analysis

Multimodal Image data banks, of normal [3] and pathological subjects, are of great utility for improving collaboration and performing research with greater statistical power. The acquisition of images is expensive and time consuming; therefore it is important to reuse them. We are currently developing human brain image data banks, one of which is likely to be the largest bank of normal aging brains in the world.

To maximise the benefits of such data banks it is necessary to investigate ways to organise and aggregate multimodal images and their associated clinical, cognitive and metadata. For derived data, provenance data should be automatically captured. Issues include: spatial registration between differently images, differentiation of the factors contributing to image variation, reliability and performance of analysis methods; and satisfaction of ethics, security and privacy policies. The knowledge representation must be open to diversity and innovation, yet support inference over the accumulated data. To gain commitment from data and method contributors, data must be identified and citable with proper attribution.

The student would pioneer data-intensive methods of automated analysis to produce metadata and to support statistical analysis, data mining and information presentation. The student would work closely with existing teams of specialists within CIVIS.