Medical Imaging on the Semantic Web: Annotation and Image Markup

Daniel Rubin, Pattanasak Mongkolwat, Vladimir Kleper, Kaustubh Supekar, David Channin

Medical images are proliferating at an explosive pace, similar to other types of data in e-Science. Technological solutions are needed to enable machines to help researchers and physicians access and use these images optimally. While Semantic Web technologies are showing promise in tackling the information challenges in biomedicine, less attention is focused on leveraging similar technologies in imaging. We are developing methods and tools to enable the transparent discovery and use of large distributed collections of medical images in cyberspace as well as within hospital information systems. Our approach is to make the human and machine descriptions of image pixel content machine-accessible through annotation using ontologies. We created an ontology of image annotation and markup, specifying the entities and relations necessary to represent the semantics of medical image pixel content. We are creating a toolkit to collect the annotations directly from researchers and physicians as they view the images on medical imaging workstations. Image annotations, represented as instances in the ontology can be serialized to a variety of formats, enabling interoperability among a variety of systems that contain images: medical records systems, image archives in hospitals, and the Semantic Web. The ontology-based annotations will enable images to be related to non-image data having related semantics and relevance. Our ultimate goal is to enable semantic integration of images and all the related scientific data pertaining to their content so that researchers and physicians can have the best understanding of the biological and physiological significance of image content.

Subjects: 11. Knowledge Representation; 11.2 Ontologies

Submitted: Jan 21, 2008

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