Arrowhead detection in biomedical images

Abstract

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 region-of-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. © 2016 Society for Imaging Science and Technology.