RadLex and Foundational Model of Anatomy (FMA) ontologies represent anatomic and image characteristics, and they are commonly used to annotate and describe contents of medical images independently of the image acquisition method (e.g., CT, MR, or US). We present ANISE, a framework that implements workows to combine these ontologies and image characteristics into Transfer Functions (TFs) that map volume density values into optical properties. Semantics encoded in the image annotations is exploited by reasoning processes to improve accuracy of TFs and the quality of the resulting image.
|Número de páginas||8|
|Publicación||CEUR Workshop Proceedings|
|Estado||Publicada - 2012|
|Evento||Joint Workshop on Semantic Technologies Applied to Biomedical Informatics and Individualized Medicine, SATBI+SWIM 2012 - In Conjunction with the 11th International Semantic Web Conference 2012, ISWC 2012 - Boston, MA, Estados Unidos|
Duración: 12 nov 2012 → 12 nov 2012