Automatic compound figure separation in scientific articles: A study of edge map and its role for stitched panel boundary detection

Abstract

We present a technique that uses edge map to separate panels from stitched compound figures appearing in biomedical scientific research articles. Since such figures may comprise images from different imaging modalities, separating them is a critical first step for effective biomedical content-based image retrieval (CBIR). We study state-of-the-art edge detection algorithms to detect gray-level pixel changes. It then applies a line vectorization process that connects prominent broken lines along the panel boundaries while eliminating insignificant line segments within the panels. We have validated our fully automatic technique on a subset of stitched multipanel biomedical figures extracted from articles within the Open Access subset of PubMed Central repository, and have achieved precision and recall of 74.20% and 71.86%, respectively, in less than 0.272 s per image, on average.