Publication
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 (Hrsg.). 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, Vol. 25, Springer-Verlag, Berlin Heidelberg, 2009.
Abstract
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.