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Proceedings Paper

Biomedical article retrieval using multimodal features and image annotations in region-based CBIR
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Paper Abstract

Biomedical images are invaluable in establishing diagnosis, acquiring technical skills, and implementing best practices in many areas of medicine. At present, images needed for instructional purposes or in support of clinical decisions appear in specialized databases and in biomedical articles, and are often not easily accessible to retrieval tools. Our goal is to automatically annotate images extracted from scientific publications with respect to their usefulness for clinical decision support and instructional purposes, and project the annotations onto images stored in databases by linking images through content-based image similarity. Authors often use text labels and pointers overlaid on figures and illustrations in the articles to highlight regions of interest (ROI). These annotations are then referenced in the caption text or figure citations in the article text. In previous research we have developed two methods (a heuristic and dynamic time warping-based methods) for localizing and recognizing such pointers on biomedical images. In this work, we add robustness to our previous efforts by using a machine learning based approach to localizing and recognizing the pointers. Identifying these can assist in extracting relevant image content at regions within the image that are likely to be highly relevant to the discussion in the article text. Image regions can then be annotated using biomedical concepts from extracted snippets of text pertaining to images in scientific biomedical articles that are identified using National Library of Medicine's Unified Medical Language System® (UMLS) Metathesaurus. The resulting regional annotation and extracted image content are then used as indices for biomedical article retrieval using the multimodal features and region-based content-based image retrieval (CBIR) techniques. The hypothesis that such an approach would improve biomedical document retrieval is validated through experiments on an expert-marked biomedical article dataset.

Paper Details

Date Published: 18 January 2010
PDF: 12 pages
Proc. SPIE 7534, Document Recognition and Retrieval XVII, 75340V (18 January 2010); doi: 10.1117/12.838973
Show Author Affiliations
Daekeun You, SUNY at Buffalo (United States)
Sameer Antani, National Library of Medicine (United States)
Dina Demner-Fushman, National Library of Medicine (United States)
Md Mahmudur Rahman, National Library of Medicine (United States)
Venu Govindaraju, SUNY at Buffalo (United States)
George R. Thoma, National Library of Medicine (United States)

Published in SPIE Proceedings Vol. 7534:
Document Recognition and Retrieval XVII
Laurence Likforman-Sulem; Gady Agam, Editor(s)

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