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Computed tomography (CT) helps the radiologist in the rapid and correct detection of a person infected with the coronavirus disease 2019 (COVID-19), and this by showing the presence of the ground-glass opacity in the lung of with the virus. Tracking the evolution of the spread of the ground-glass opacity (GGO) in the lung of the person infected with the virus needs to study more than one image in different times. The various CT images must be registration to identify the evolution of the ground glass in the lung and to facilitate the study and identification of the virus. Due to the process of registration images is essentially an improvement problem, we present in this paper a new HPSGWO algorithm for registration CT images of a lung infected with the COVID-19. This algorithm is a hybridization of the two algorithms Particle swarm optimization (PSO) and Grey wolf optimizer (GWO). The simulation results obtained after applying the algorithm to the test images show that the proposed approach achieved high-precision and robust registration compared to other methods such as GWO, PSO, Firefly Algorithm (FA), and Crow Searcha Algorithms (CSA).

In recent years, the optimization problem using meta-heuristic algorithms has been widely used in medical image registration and was a solution in diagnosing many diseases and tumors. Given the great success achieved by the sine cosine algorithm (SCA) and particle swarm optimization (PSO) algorithms in many medical images analysis, and the use of the computed tomography (CT) scan images for diagnosing COVID-19 patients, we propose an improved sine cosine algorithm (ISCA) resulting from the hybridization of the SCA and PSO algorithms to register the CT images of the lung of the people infected by COVID-19. Simulation results show that the proposed approach can achieve high accuracy and robust recording compared to the SCA method. Graphical abstract