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

Abstract

Coronavirus disease 2019 (COVID-19) is pervasive worldwide, posing a high risk to people’s safety and health. Many algorithms were developed to identify COVID-19. One way of identifying COVID-19 is by computed tomography (CT) images. Some segmentation methods are proposed to extract regions of interest from COVID-19 CT images to improve the classification. In this paper, an efficient version of the recent manta ray foraging optimization (MRFO) algorithm is proposed based on the oppositionbased learning called the MRFO-OBL algorithm. The original MRFO algorithm can stagnate in local optima and requires further exploration with adequate exploitation. Thus, to improve the population variety in the search space, we applied Opposition-based learning (OBL) in the MRFO’s initialization step. MRFO-OBL algorithm can solve the image segmentation problem using multilevel thresholding. The proposed MRFO-OBL is evaluated using Otsu’s method over the COVID-19 CT images and compared with six meta-heuristic algorithms: sine-cosine algorithm, moth flame optimization, equilibrium optimization, whale optimization algorithm, slap swarm algorithm, and original MRFO algorithm. MRFO-OBL obtained useful and accurate results in quality, consistency, and evaluation matrices, such as peak signal-to-noise ratio and structural similarity index. Eventually, MRFO-OBL obtained more robustness for the segmentation than all other algorithms compared. The experimental results demonstrate that the proposed method outperforms the original MRFO and the other compared algorithms under Otsu’s method for all the used metrics.

Nature-inspired optimization algorithms and their significance in multi-thresholding image segmentation: an inclusive review

“…Though, more research and experiments need to be carried out to determine suitable objective functions to finally regulate optimal threshold values 10 Modified Whale Optimization Algorithm (MWOA) Otsu’s thresholding and Kapur entropy Anitha et al ( 2021 ) Standard Color Images Proposed method is compared with GA, PSO, ABC and CS PSNR, SSIM, FSIM and CPU time The proposed MWOA offers improved performance as compare to other algorithms consuming a reduced amount of computational time 11 Volleyball Premier League using Whale Optimization Algorithm (VPLWOA) Otsu’s thresholding Abd Elaziz et al ( 2021a ) Standard Gray Images Proposed method is compared with SSO, FFA, WOA, VPL and SCA PSNR, SSIM, FSIM and CPU time The sports inspiration based on basic VPL is applied for the very first time in the field of multilevel thresholding. Experimental results confirm that the proposed algorithm outdoes other existing meta-heuristic algorithms 12 Manta Ray Foraging Optimization based on the Opposition-Based Learning (MRFO-OBL) Otsu’s thresholding Houssein et al ( 2021b ) Computed Tomography (CT) Images: Medical Images Proposed method is compared with SCA, MFO, EO, WOA, SSO and MRFO PSNR and SSIM The proposed MRFO-OBL basically finds the finest threshold values to further maximize Otsu’s function. However, in future hybridization mechanism can be tried and implemented by amalgamating proposed method with other optimization techniques 13 Bat Algorithm (BA) Otsu thresholding and Kapur’s entropy Yang et al ( 2021b ) Gray scale images Not Compared PSNR and SSIM The experiment results show that Otsu based method is more suitable for multi-level threshold image segmentation 14 A new entropy measure, called the t-entropy t-entropy Chakraborty et al ( 2021 ) ...…”

Enhancement and segmentation of medical images through pythagorean fuzzy sets-An innovative approach

“…The capability of the proposed PFS-based thresholding scheme has been inspected in several lungs affected due to COVID-19 (above 50% and below 50%) images. In order to exhibit the great performance of the proposed scheme, it is compared with four methods namely Method1 [ 4 ], Method2 [ 5 ], Method3 [ 6 ], and Method4 [ 7 ], several quality measurement factors examined in the before-mentioned section have been applied. The values of the quality measurement factors reveal that the outcomes of the proposed method produce a greater efficiency than recent sophisticated methods.…”

“…5 (1b) –(2j)) are thresholded, and these depicted thresholds are listed in rows second to twenty and columns second to sixth in Table 1 . By presenting the methods of Method1 [ 4 ], Method2 [ 5 ], Method3 [ 6 ], and Method4 [ 7 ], the foregrounds of the enhanced images (Fig. 5 (1b)–(2j)) are separated and these foregrounds are pictured in Fig.…”

“…Further, enhanced images are employed for the purpose of the extraction process, which determines the thresholds that separate the object from the background. Furthermore, the proposed and other object extraction schemes, such as Method1 [ 4 ], Method2 [ 5 ], Method3 [ 6 ], Method4 [ 7 ] are compared with each other with the aid of quality measurement factors. According to the results of the aforementioned analysis, the proposed PFS-based object extraction technique performs admirably in terms of segmentation and factor values.…”

Precise Segmentation of COVID-19 Infected Lung from CT Images Based on Adaptive First-Order Appearance Model with Morphological/Anatomical Constraints

“…Furthermore, multiple refinement stages based on machine learning classification and neighboring anatomy-guided learning mechanisms were included in their system to detect pathological regions during FC segmentation. A recent study by Houssein et al [21] developed a segmentation system that employed a heuristic method, called manta ray foraging optimization (MRFO), based on an opposition-based learning (OBL), using Otsu's method as a fitness function, to get the best threshold values using COVID-19 CT images. More information about textureand shape-based lung segmentation can be found in [22].…”