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

Abstract:

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.

Año de publicación:

2012

Keywords:

Fuente:

scopus