Image registration of computed tomography of lung infected with COVID-19 using an improved sine cosine algorithm

• Published in: Medical & biological engineering & computing Vol. 60; no. 9; pp. 2521 - 2535
• Main Authors: Dida, Hedifa, Charif, Fella, Benchabane, Abderrazak
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer Berlin Heidelberg 01.09.2022; Springer Nature B.V
• Subjects: Algorithms; Bioengineering; Biomedical and Life Sciences; Biomedical Engineering and Bioengineering; Biomedicine; Computed tomography; Computer Applications; Coronaviruses; COVID-19; Disease transmission; Heuristic methods; Human Physiology; Hybridization; Image registration; Imaging; Lungs; Medical imaging; Medical research; Methods; Optimization algorithms; Optimization techniques; Original; Original Article; Particle swarm optimization; Radiology; Registration; Similarity measures; Tomography; Trigonometric functions; Tumors; COVID-19; Image registration; Sine cosine algorithm; Particle swarm optimization
• ISSN: 0140-0118; 1741-0444; 1741-0444
• DOI: 10.1007/s11517-022-02606-z

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