Presented at: 9th Extended Semantic Web Conference (ESWC2012)
We present ANISE and illustrate the benefits of exploiting knowledge encoded in controlled vocabularies or ontologies to precisely visualize 3D Medical images. ANISE receives 3D images annotated with existing medical ontologies as for example, RadLex and the Foundational Model of Anatomy (FMA), and performs reasoning tasks to improve the effectiveness of the visualization of organs or tissues of interest presented in the input data. We show the quality of ANISE rendering result on a Computed Tomography Head data. Attendees will be able to observe the benefits of using semantic annotations and the precision achieved by the same visualization tool when these annotations are considered for volume rendering.
Keywords: Medical Ontologies, Semantic Visualizer, Semantically Annotated Medical Images
Resource URI on the dog food server: http://data.semanticweb.org/conference/eswc/2012/paper/poster/360
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