An enhanced ant colony optimizer with Cauchy-Gaussian fusion and novel movement strategy for multi-threshold COVID-19 X-ray image segmentation - Zhao, Xiuzhi; Liu, Lei; Heidari, Ali Asghar; Chen, Yi; Ma, Benedict Jun; Chen, Huiling; Quan, Shichao (Frontiers in Neuroinformatics, 2023)
The novel coronavirus pneumonia (COVID-19) is a respiratory disease that is 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 important to be able to separate and identify lesions from its pathology images regardless of the computer-aided diagnosis techniques. Therefore, image segmentation in the preprocessing 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 firstly proposed. In MGACO, not only a new move strategy is introduced, but also the Cauchy-Gaussian fusion strategy is incorporated. Due to their introduction, it has been accelerated in terms of convergence speed and has resulted in a significant enhancement of its ability to jump out of the local optimum. Furthermore, a 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 strongly demonstrate the performance of MGACO, we not only qualitatively analyze it in detail, but also compare it with other peers on 30 benchmark functions from IEEE CEC2014, which proves that it has 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 between it and 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 images 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.