Unimodal thresholding for Laplacian-based Canny–Deriche filter

A unimodal thresholding method for the Laplacian-based Canny–Deriche edge detector featuring a double-thresholding approach and reconstruction strategy was proposed. In this method, an improved image segmentation technique derived from an image histogram was developed. The accuracy of the segmentation was compared with the Otsu, Rosin, and Canny-hysteresis techniques. It was shown that the proposed method is more robust and accurate in detecting edges, resulting in a sensitivity of consistently more than 17.1%, with a standard deviation of less than 0.087, and a figure of merit (FOM) greater than 0.787 for all images tested in this study.

► A unimodal thresholding method for the Laplacian-based Canny–Deriche edge detector was proposed.
► An improved segmentation technique derived from image histogram was developed.
► The accuracy of the segmentation was compared with standard Otsu, Rosin, and Canny-hysteresis techniques.