Changes between Version 4 and Version 5 of RadiologyLexicon


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Timestamp:
07/23/08 11:05:35 (10 years ago)
Author:
onardmejino
Comment:

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  • RadiologyLexicon

    v4 v5  
    1 RadLex is a controlled terminology project from the Radiological Society of North America (RSNA) designed to provide a lexicon for uniform indexing and retrieval of radiology information resources. However advancing imaging technology and support systems (processing, storage, delivery of images) as well as the tremendous increase in volume of images being produced require a more sophisticated organizational structure to meet the growing computational and informatics demands in carrying out radiology tasks. It requires ontological enhancement to address those needs. As proof concept, we augmented selected portions of the anatomy axis of RadLex by importing subsets of the FMA to provide RadLex a more robust and expressive semantic framework that can sufficiently capture and accommodate any and all salient anatomical information needed for radiology-related tasks. 
     1RadLex is a controlled terminology project from the Radiological Society of North America (RSNA) designed to provide a lexicon for uniform indexing and retrieval of radiology information resources. However advancing imaging technology and support systems (processing, storage, delivery of images) as well as the tremendous increase in volume of images being produced require a more sophisticated organizational structure to meet the growing computational and informatics demands in carrying out radiology tasks. It requires ontological enhancement to address those needs. As proof concept, we augmented selected portions of the anatomy axis of RadLex by importing subsets of the FMA to provide RadLex a more robust and expressive semantic framework that can sufficiently capture and accommodate any and all salient anatomical information needed for radiology-related tasks. We performed the operation manually to serve as the test model for the automatic view generation methods that our group is developing. From this exercise we gain insights on the requirements and operations needed to automate the view extraction.  
    22 
    33You can find details of our preliminary work in the following publication: