<title>Exploring use of images in clinical articles for decision support in evidence-based medicine</title>

Abstract

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%.

Separating compound figures in journal articles to allow for subfigure classification

“…according to prior manual classification 14 or combining separate methods and using captions to estimate the number of subfigures. 12 An algorithm for text label extraction and identification was proposed by You et al 15 that shows good accuracy of subfigure label extraction.…”

Figure content analysis for improved biomedical article retrieval

“…Extracting these symbols may assist in measuring locally relevant image features for effective (and possibly semantically relevant) content-based image retrieval (CBIR). In previous research we have reported an accuracy of 72.02% in localization of text and symbols on extracted panel images 4 .…”

Automatic Detection of Biomedical Compound Figure using Bag of Words

“…There are indications that the search experience of such databases can be enhanced by incorporating images as part of the search [1,2]. Inspired by the success of Content-Based Image Retrieval (CBIR) [3], the integration of images and text in a search engine has received a remarkable attention in biomedical article retrieval [4][5][6]. Images in biomedical publications constitute a considerable amount of information that can assist clinicians or medical researchers to evaluate potential usefulness of an article in a clinical situation at hand.…”