Figure content analysis for improved biomedical article retrieval

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.