Scalable Medical Image Understanding by Fusing Cross-Modal Object Recognition with Formal Domain Semantics

Manuel Möller, Michael Sintek, Paul Buitelaar, Saikat Mukherjee, Xiang Sean Zhou, Jörg Freund

In: A. Fred, J. Filipe, H. Gamboa (editor). Best papers of BIOSTEC 2008. International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC-2008) Revised Selected Papers January 28-31 Funchal Madeira Portugal Pages 390-401 Communications in Computer and Information Science 25 Springer-Verlag Berlin Heidelberg 2009.


Recent advances in medical imaging technology have dramatically increased the amount of clinical image data. In contrast, techniques for efficiently exploiting the rich semantic information in medical images have evolved much slower. Despite the research outcomes in image understanding, current image databases are still indexed by manually assigned subjective keywords instead of the semantics of the images. Indeed, most current content-based image search applications index image features that do not generalize well and use inflexible queries. This slow progress is due to the lack of scalable and generic information representation systems which can abstract over the high dimensional nature of medical images as well as semantically model the results of object recognition techniques. We propose a system combining medical imaging information with ontological formalized semantic knowledge that provides a basis for building universal knowledge repositories and gives clinicians fully cross-lingual and cross-modal access to biomedical information.


German Research Center for Artificial Intelligence
Deutsches Forschungszentrum für Künstliche Intelligenz