Edge detection is an important step for finding the discontinuities of images and detecting the boundaries of objects. This work presents a novel algorithm for image edge detection using ant colony optimization and Fisher ratio (F ratio)-based techniques. Ants generally search the food from the nest to the food source in the way that maximizes the intensity of pheromone (a chemical secretion). The proposed technique considers that the movements of the artificial ants are steered by the local intensity variation in the image pixel. The directions of ants movements in the image are determined using a direction probability matrix, computed by pheromone and heuristic information of possible directions. In this work, F ratio technique is utilized to determine the optimum threshold value from updated pheromone matrix. This threshold value is further used to extract binary edge map from pheromone matrix. The experiment is conducted on the different test images, i.e., Cameraman, Lena, Coins, Peppers, House and Pillsetc image. The proposed edge detection algorithm is evaluated on the basis of statistical parameters such as kappa, figure of merit, Baddeley’s delta metric and Hausdorff distance, and the experimental results show that the proposed method performs better as compared to earlier reported techniques in most of the cases.