2008PubMedRequires access

UMLS-based automatic image indexing.

Charles Sneiderman, Charles Alan Sneiderman, D Demner-Fushman, Dina Demner-Fushman, K W Fung, Kin Wah Fung, B Bray, Bruce Bray

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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.

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What this paper is about

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.

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Available 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.

Key concepts: Search engine indexing, Unified Medical Language System, Computer science, Information retrieval, Image retrieval, Index (typography), Image (mathematics), Automatic indexing

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