It’s like ants working together to solve complex problems! Just like these industrious insects, scientists have developed an enhanced ant colony optimizer (MGACO) with a fusion of Cauchy-Gaussian strategies and a novel movement technique. This advanced algorithm is designed to segment COVID-19 pathology images, making it easier to identify and analyze lesions. By applying multi-threshold image segmentation (MIS), MGACO-MIS proves to be highly effective in pre-processing COVID-19 images. It outperforms other segmentation methods and demonstrates strong adaptability to different threshold levels. The research compared MGACO with other optimization algorithms using 30 benchmark functions, showing its superior problem-solving capabilities. Moreover, the evaluations conducted on real COVID-19 pathology images confirm the exceptional performance of MGACO-MIS in obtaining high-quality segmentation results. These findings not only establish MGACO as an outstanding swarm intelligence optimization algorithm but also highlight the potential of MGACO-MIS as an excellent segmentation method. Get ready to be impressed by the power of ants and dive deeper into the fascinating world of image segmentation in COVID-19 pathology research!

The novel coronavirus pneumonia (COVID-19) is a respiratory disease of great concern in terms of its dissemination and severity, for which X-ray imaging-based diagnosis is one of the effective complementary diagnostic methods. It is essential to be able to separate and identify lesions from their pathology images regardless of the computer-aided diagnosis techniques. Therefore, image segmentation in the pre-processing stage of COVID-19 pathology images would be more helpful for effective analysis. In this paper, to achieve highly effective pre-processing of COVID-19 pathological images by using multi-threshold image segmentation (MIS), an enhanced version of ant colony optimization for continuous domains (MGACO) is first proposed. In MGACO, not only a new move strategy is introduced, but also the Cauchy-Gaussian fusion strategy is incorporated. It has been accelerated in terms of convergence speed and has significantly enhanced its ability to jump out of the local optimum. Furthermore, an MIS method (MGACO-MIS) based on MGACO is developed, where it applies the non-local means, 2D histogram as the basis, and employs 2D Kapur’s entropy as the fitness function. To demonstrate the performance of MGACO, we qualitatively analyze it in detail and compare it with other peers on 30 benchmark functions from IEEE CEC2014, which proves that it has a stronger capability of solving problems over the original ant colony optimization for continuous domains. To verify the segmentation effect of MGACO-MIS, we conducted a comparison experiment with eight other similar segmentation methods based on real pathology images of COVID-19 at different threshold levels. The final evaluation and analysis results fully demonstrate that the developed MGACO-MIS is sufficient to obtain high-quality segmentation results in the COVID-19 image segmentation and has stronger adaptability to different threshold levels than other methods. Therefore, it has been well-proven that MGACO is an excellent swarm intelligence optimization algorithm, and MGACO-MIS is also an excellent segmentation method.

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