Many widely used digital medical image collections have been established but these are generally used as raw data
sources without related image analysis toolsets. Providing associated functionality to allow specific types of operations
to be performed on these images has proved beneficial in some cases (e.g. brain image registration and atlases).
However, toolset development to provide generic image analysis functions on medical images has tended to be ad hoc,
with Open Source options proliferating (e.g. ITK).
Our Automated Medical Image Collection Annotation (AMICA) system is both an image repository, to which the
research community can contribute image datasets, and a search/retrieval system that uses automated image annotation.
AMICA was designed for the Windows Azure platform to leverage the flexibility and scalability of the cloud. It is
intended that AMICA will expand beyond its initial pilot implementation (for brain CT, MR images) to accommodate a
wide range of modalities and anatomical regions.
This initiative aims to contribute to advances in clinical research by permitting a broader use and reuse of medical image
data than is currently attainable. For example, cohort studies for cases with particular physiological or phenotypical
profiles will be able to source and include enough cases to provide high statistical power, allowing more individualised
risk factors to be assessed and thus allowing screening and staging processes to be optimised. Also, education, training
and credentialing of clinicians in image interpretation, will be more effective because it will be possible to select
instances of images with specific visual aspects, or correspond to types of cases where reading performance
improvement is desirable.