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%.
Highlights
Sensitive ant colony optimization (SACO) for medical image edge detection is introduced to improve the analysis of computed tomography (CT) and X-ray images
Hand X-ray) while comparing ACO and SACO for 300,000 iterations and the considered demicontractive operators are included in Figure 2; in the last image, the original medical images are overlapped with the best solutions
As classical algorithms have less performance within image edge detection, metaheuristics are used for feasible solutions
Summary
The current paper introduces a version of ant colony method with a specific feature called pheromone sensitivity level, PSL, for solving the medical edge detection problem. Pintea and Ticala proposed the first related theoretical approach in [17]; a step forward was made in [18] It includes more tests for both ant colony versions of medical image edge detection and a comparison of these techniques; details, including the efficiency of the new parameters and the use of some demicontractive operators, are presented. Sensitive ant colony optimization (SACO) for medical image edge detection is introduced to improve the analysis of CT and X-ray images. The section includes the present work’s prerequisites with mathematical support, the edge detecting problem and the sensitive ant colony optimization (SACO) method. Future work and arguments regarding the benefits of ACO and SACO for medical images conclude the present study
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
