Figure content analysis for improved biomedical article retrieval

Daekeun You; Emilia Apostolova; Sameer Antani; Dina Demner-Fushman; George R. Thoma

doi:10.1117/12.805976

19 January 2009 Figure content analysis for improved biomedical article retrieval

Daekeun You, Emilia Apostolova, Sameer Antani, Dina Demner-Fushman, George R. Thoma

Proceedings Volume 7247, Document Recognition and Retrieval XVI; 72470V (2009) https://doi.org/10.1117/12.805976
Event: IS&T/SPIE Electronic Imaging, 2009, San Jose, California, United States

Abstract

Biomedical images are invaluable in medical education and establishing clinical diagnosis. Clinical decision support (CDS) can be improved by combining biomedical text with automatically annotated images extracted from relevant biomedical publications. In a previous study we reported 76.6% accuracy using supervised machine learning on the feasibility of automatically classifying images by combining figure captions and image content for usefulness in finding clinical evidence. Image content extraction is traditionally applied on entire images or on pre-determined image regions. Figure images articles vary greatly limiting benefit of whole image extraction beyond gross categorization for CDS due to the large variety. However, text annotations and pointers on them indicate regions of interest (ROI) that are then referenced in the caption or discussion in the article text. We have previously reported 72.02% accuracy in text and symbols localization but we failed to take advantage of the referenced image locality. In this work we combine article text analysis and figure image analysis for localizing pointer (arrows, symbols) to extract ROI pointed that can then be used to measure meaningful image content and associate it with the identified biomedical concepts for improved (text and image) content-based retrieval of biomedical articles. Biomedical concepts are identified using National Library of Medicine's Unified Medical Language System (UMLS) Metathesaurus. Our methods report an average precision and recall of 92.3% and 75.3%, respectively on identifying pointing symbols in images from a randomly selected image subset made available through the ImageCLEF 2008 campaign.

Citation Download Citation

Daekeun You, Emilia Apostolova, Sameer Antani, Dina Demner-Fushman, and George R. Thoma "Figure content analysis for improved biomedical article retrieval", Proc. SPIE 7247, Document Recognition and Retrieval XVI, 72470V (19 January 2009); https://doi.org/10.1117/12.805976

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available