**1. Introduction**

Today, medicine is interconnected with technology. Human injuries caused by accidents or other similar events can be detected and correctly diagnosed by using tomography or X-rays. In medical image processing, edge detection has a major role. In order to obtain accurate medical diagnoses, the best computing models are involved. As swarm intelligence has a huge impact nowadays in solving complex problems, the current work uses a particular swarm method, ant colony optimization (ACO) [1], to solve the edge detection problem.

Learning is one of the most efficient artificial intelligence capabilities; in [2], learning with PDE-based CNNs and dense nets for the purpose of detecting COVID-19, pneumonia, and tuberculosis from chest X-ray images was studied. In the same context, automatic COVID-19 detection from chest X-ray and CT-scan images was proposed [3] within a new meta-heuristic feature selection using an optimized convolutional neural network [4].

"A meta-heuristic is an iterative master process that guides and modifies the operations of subordinate heuristics to efficiently produce high-quality solutions" [5]. In general, the quality of heuristics solutions, including bio-inspired methods such as ACO, is given by appropriate probabilistic assumptions [6].

One of the most recent works related to medical image edge detection with ant colony optimization shows the efficiency of a gradient-based ant spread modification to ACO for retinal blood vessel edge detection [7]. In [8], a new image filtering method is introduced for the problem of edge extraction for some targets according to the top-down information based on the image perspective effect; the authors assign scale and orientation, in a hard manner, in order to enhance a local edge detection.

Ant colony optimization is one of the most successful metaheuristics used within complex combinatorial optimization problems, as, for example, in scheduling, transportation

**Citation:** Ticala, C.; Pintea, C.-M.; Matei, O. Sensitive Ant Algorithm for Edge Detection in Medical Images. *Appl. Sci.* **2021**, *11*, 11303. https:// doi.org/10.3390/app112311303

Academic Editor: Peng-Yeng Yin, Ray-I Chang, Youcef Gheraibia, Ming-Chin Chuang, Hua-Yi Lin and Jen-Chun Lee

Received: 1 November 2021 Accepted: 22 November 2021 Published: 29 November 2021

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**Copyright:** © 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).

and test case prioritization problems [9–12]. Pintea and Pop introduced and showed the benefits of agen<sup>t</sup> sensitivity, including sensitive robots, in security-related problems [13,14], such as a denial jamming attack on sensor networks. As a reference, the current work makes use of the state-of-the-art Canny [15] edge detection technique. Recently, the Canny operator was used in [16] during a symmetrical difference kernel SAR image edge detection process.

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. The artificial ants are endowed with different levels of artificial pheromone sensitivity; thus, the agents have different reactions in a dynamic environment. Here, the new algorithm is applied to the image edge problem and requires a heuristic value computed with two admissible perturbation operators applied to a demicontractive mapping.

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.

The current work's content is as follows:


The next section includes the present work's prerequisites with mathematical support, the edge detecting problem and the sensitive ant colony optimization (SACO) method. The numerical tests and bio-inspired methods results follows in Section 3. The comparison of methods, the operators' behavior and the representation of medical image results are discussed in Section 4. Future work and arguments regarding the benefits of ACO and SACO for medical images conclude the present study.
