Proceedings Article | 25 March 2016
KEYWORDS: Medical imaging, Medical research, Databases, Image processing, Breast cancer, Cancer, Data acquisition, Information science, Medicine, Imaging informatics, Data modeling, Mammography, Breast, Image storage
Due to the huge amount of research involving medical images, there is a widely accepted need for
comprehensive collections of medical images to be made available for research. This demand led to the
design and implementation of a flexible image repository, which retrospectively collects images and data
from multiple sites throughout the UK. The OPTIMAM Medical Image Database (OMI-DB) was created to
provide a centralized, fully annotated dataset for research. The database contains both processed and
unprocessed images, associated data, annotations and expert-determined ground truths. Collection has been
ongoing for over three years, providing the opportunity to collect sequential imaging events. Extensive
alterations to the identification, collection, processing and storage arms of the system have been undertaken to
support the introduction of sequential events, including interval cancers.
These updates to the collection systems allow the acquisition of many more images, but more importantly,
allow one to build on the existing high-dimensional data stored in the OMI-DB. A research dataset of this
scale, which includes original normal and subsequent malignant cases along with expert derived and clinical
annotations, is currently unique. These data provide a powerful resource for future research and has initiated
new research projects, amongst which, is the quantification of normal cases by applying a large number of
quantitative imaging features, with a priori knowledge that eventually these cases develop a malignancy. This
paper describes, extensions to the OMI-DB collection systems and tools and discusses the prospective
applications of having such a rich dataset for future research applications.
