An Improved Marine Predators Algorithm With Fuzzy Entropy for Multi-Level Thresholding: Real World Example of COVID-19 CT Image Segmentation

Abstract

Medical imaging techniques play a critical role in diagnosing diseases and patient healthcare. They help in treatment, diagnosis, and early detection. Image segmentation is one of the most important steps in processing medical images, and it has been widely used in many applications. Multi-level thresholding (MLT) is considered as one of the simplest and most effective image segmentation techniques. Traditional approaches apply histogram methods; however, these methods face some challenges. In recent years, swarm intelligence methods have been leveraged in MLT, which is considered an NP-hard problem. One of the main drawbacks of the SI methods is when searching for optimum solutions, and some may get stuck in local optima. This because during the run of SI methods, they create random sequences among different operators. In this study, we propose a hybrid SI based approach that combines the features of two SI methods, marine predators algorithm (MPA) and mothflame optimization (MFO). The proposed approach is called MPAMFO, in which, the MFO is utilized as a local search method for MPA to avoid trapping at local optima. The MPAMFO is proposed as an MLT approach for image segmentation, which showed excellent performance in all experiments. To test the performance of MPAMFO, two experiments were carried out. The first one is to segment ten natural gray-scale images. The second experiment tested the MPAMFO for a real-world application, such as CT images of COVID-19. Therefore, thirteen CT images were used to test the performance of MPAMFO. Furthermore, extensive comparisons with several SI methods have been implemented to examine the quality and the performance of the MPAMFO. Overall experimental results confirm that the MPAMFO is an efficient MLT approach that approved its superiority over other existing methods.

Multilevel thresholding image segmentation based on improved volleyball premier league algorithm using whale optimization algorithm

“…With the fast propagation of COVID-19, several researchers presented many solutions for the detection and segmentation of chest CT gray-level images. In [ 13 ], the authors proposed a new version of the marine predator’s algorithm (MPA) improved by MFO based on fuzzy entropy. The proposed method MPAMFO presented their efficiency compared to the existing swarm intelligence works in terms of PSNR and SSIM.…”

Improved manta ray foraging optimization for multi-level thresholding using COVID-19 CT images

“…It is invasive and time-consuming, and the diagnosis is false-negative. Chest computed tomography (CT) is another test that plays an essential role in COVID-19 diagnosis [ 7 ].…”

“…Preliminary studies suggested that chest CT has a high detection sensitivity for COVID-19 lung pathology. Several groups have demonstrated the capacity for diagnosis based on CAD systems, with accuracy as high as 95% [ 7 ]. Medical imaging has been used recently for multiple diagnoses of the disease.…”

Automatic clustering method to segment COVID-19 CT images

“…Diagnosing COVID-19 is a critical challenge for health organizations that must be accurately and efficiently implemented to make necessary plans [3]. The real-time polymerase chain reaction (RT-PCR) can be used to diagnose COVID-19, but it is a time-consuming test; also, it may suffer from false-negative diagnosing [4,5]. Therefore, medical imaging, such as chest X-ray and chest Computed Tomography (CT) can be used efficiently for diagnosing Image segmentation is considered as an important key for analyzing medical images.…”