title: UMLS-based automatic image indexing.
abstract: To date, most accurate image retrieval techniques rely on textual descriptions of images. Our goal is to automatically generate indexing terms for an image extracted from a biomedical article by identifying Unified Medical Language System (UMLS) concepts in image caption and its discussion in the text. In a pilot evaluation of the suggested image indexing method by five physicians, a third of the automatically identified index terms were found suitable for indexing.
authors.affiliation: National Library of Medicine, NIH, DHHS, Bethesda, MD, USA.
authors.forename: C
authors.initials: C
authors.lastname: Sneiderman
authors.forename: Charles Alan
authors.initials: CA
authors.lastname: Sneiderman
authors.forename: D
authors.initials: D
authors.lastname: Demner-Fushman
authors.forename: Dina
authors.initials: D
authors.lastname: Demner-Fushman
authors.forename: K W
authors.initials: KW
authors.lastname: Fung
authors.forename: Kin Wah
authors.initials: KW
authors.lastname: Fung
authors.forename: B
authors.initials: B
authors.lastname: Bray
authors.forename: Bruce
authors.initials: B
authors.lastname: Bray
mesh_terms: D001185:Artificial Intelligence; D003628:Database Management Systems; D004282:Documentation / Q000379:methods*; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D016247:Information Storage and Retrieval / Q000379:methods; D008396:Maryland; D009323:Natural Language Processing; D010363:Pattern Recognition, Automated / Q000379:methods*; D011873:Radiology Information Systems; D009626:Terminology as Topic; D017432:Unified Medical Language System