A workow for improving medical visualization of semantically annotated CT-images

Alexander Baranya, Luis Landaeta, Alexandra La Cruz, Maria Esther Vidal

Resultado de la investigación: Contribución a una revistaArtículo de la conferenciarevisión exhaustiva

2 Citas (Scopus)

Resumen

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.

Idioma originalInglés
Páginas (desde-hasta)24-31
Número de páginas8
PublicaciónCEUR Workshop Proceedings
Volumen930
EstadoPublicada - 2012
EventoJoint 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 201212 nov 2012

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