Sensitive Ant Algorithm for Edge Detection in Medical Images
Cristina Ţicală, Camelia-M. Pintea, Oliviu Matei
Abstract
Nowadays, reliable medical diagnostics from computed tomography (CT) and X-rays can be obtained by using a large number of image edge detection methods. One technique with a high potential to improve the edge detection of images is ant colony optimization (ACO). In order to increase both the quality and the stability of image edge detection, a vector called pheromone sensitivity level, PSL, was used within ACO. Each ant in the algorithm has one assigned element from PSL, representing the ant’s sensibility to the artificial pheromone. A matrix of artificial pheromone with the edge information of the image is built during the process. Demi-contractions in terms of the mathematical admissible perturbation are also used in order to obtain feasible results. In order to enhance the edge results, post-processing with the DeNoise convolutional neural network (DnCNN) was performed. When compared with Canny edge detection and similar techniques, the sensitive ACO model was found to obtain overall better results for the tested medical images; it outperformed the Canny edge detector by 37.76%.