Arrowhead detection in biomedical images.
KC S, Naved A, Roy PP, Wendling L, Antani SK, Thoma GR
Proceedings IS&T Electronic Imaging, Document Recognition and Retrieval XXIII, 2016, pp. 1-7.
Abstract:
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.
KC S, Naved A, Roy PP, Wendling L, Antani SK, Thoma GR. Arrowhead detection in biomedical images.
Proceedings IS&T Electronic Imaging, Document Recognition and Retrieval XXIII, 2016, pp. 1-7.