Medical images in biomedical documents tend to be complex by nature and often contain several regions that are annotated using arrows. Arrowhead detection is a critical precursor to regionof-interest (ROI) labeling and image content analysis. To detect arrowheads, images are first binarized using fuzzy binarization technique to segment a set of candidates based on connected component principle. To select arrow candidates, we use convexity defect-based filtering, which is followed by template matching via dynamic programming. The similarity score via dynamic time warping (DTW) confirms the presence of arrows in the image. Our test on biomedical images from imageCLEF 2010 collection shows the interest of the technique.
K.C. Santosh, Naved Alam, Partha Pratim Roy, Laurent Wendling, Sameer Antani, George R. Thoma, "Arrowhead detection in biomedical images" in Proc. IS&T Int’l. Symp. on Electronic Imaging: Document Recognition and Retrieval XXIII, 2016, https://doi.org/10.2352/ISSN.2470-1173.2016.17.DRR-054