Essential information is often conveyed pictorially (images, illustrations, graphs, charts, etc.) in biomedical
publications. A clinician's decision to access the full text when searching for evidence in support of clinical decision
is frequently based solely on a short bibliographic reference. We seek to automatically augment these references
with images from the article that may assist in finding evidence.
In a previous study, the feasibility of automatically classifying images by usefulness (utility) in finding evidence
was explored using supervised machine learning and achieved 84.3% accuracy using image captions for modality and
76.6% accuracy combining captions and image data for utility on 743 images from articles over 2 years from a
clinical journal. Our results indicated that automatic augmentation of bibliographic references with relevant images
was feasible. Other research in this area has determined improved user experience by showing images in addition to
the short bibliographic reference. Multi-panel images used in our study had to be manually pre-processed for image
analysis, however. Additionally, all image-text on figures was ignored.
In this article, we report on developed methods for automatic multi-panel image segmentation using not only image
features, but also clues from text analysis applied to figure captions. In initial experiments on 516 figure images we
obtained 95.54% accuracy in correctly identifying and segmenting the sub-images. The errors were flagged as
disagreements with automatic parsing of figure caption text allowing for supervised segmentation. For localizing
text and symbols, on a randomly selected test set of 100 single panel images our methods reported, on the average,
precision and recall of 78.42% and 89.38%, respectively, with an accuracy of 72.02%.