A Simple and Efficient Arrowhead Detection Technique in Biomedical Images
KC Santosh, Naved Alam, Partha Pratim Roy, Laurent Wendling, Sameer Antani, George R. Thoma · International Journal of Pattern Recognition and Artificial Intelligence · 2016
In biomedical documents/publications, medical images tend to be complex by nature and often contain several regions that are annotated using arrows. In this context, an automated arrowhead detection is a critical precursor to region-of-interest (ROI) labeling and image content analysis. To detect arrowheads, in this paper, images are first binarized using fuzzy binarization technique to segment a set of candidates based on connected component (CC) principle. To select arrow candidates, we use convexity defect-based filtering, which is followed by template matching via dynamic time warping (DTW). The DTW similarity score confirms the presence of arrows in the image. Our test results on biomedical images from imageCLEF 2010 collection shows the interest of the technique, and can be compared with previously reported state-of-the-art results.