**5. Conclusions**

Medical image edge detection is nowadays a must in the context of pandemics and other illness and injuries. As classical algorithms have less performance within image edge detection, metaheuristics are used for feasible solutions. The current paper shows the efficiency of bio-inspired algorithms, in particular of the ant-based technique, with an emphasis on the sensitive version of *Ant Colony Optimization (ACO)*.

Sensitivity plays a crucial role in the exploration and exploitation of ants' solutions within the environment; the sensitivity level starts with the maximum level of sensitivity, one, per the ACO level of sensitivity, and during the processes, the ant's level of sensitivity changes. they become less or more sensitive to the environment based on the PSL probability, which is influenced by ants behavior and image intensity.

Nevertheless, the demicontractive operators shows their utility in edge detection problems; an analysis of the results with the presented operators shows how the results vary based on the operators' features.

The edges obtained with each considered operator were overlapped over the original images. The majority of edges were superposed, following the CT and X-ray original bone lines.

**Author Contributions:** Conceptualization, investigation C.T. and C.-M.P.; methodology formal analysis C.T. and O.M., software, C.T.; validation C.T. and O.M.; writing—original draft preparation C.T. and C.-M.P. and O.M.; writing—review and editing C.T. and C.-M.P. All authors have read and agreed to the published version of the manuscript.

**Funding:** This research received no external funding.

**Conflicts of Interest:** The authors declare no conflict of interest.
