Table 1.
Some recent methods proposed for ISP
| Algorithms | Contributions and disadvantages |
|---|---|
| Hybrid slime mould optimizer with whale optimization algorithm (HSMA_WOA) (Abdel-Basset et al. 2020) | Contributions |
| – This paper proposed a new image segmentation algorithm based on integrating the slime mould algorithm (SMA) with the whale optimization algorithm for segmenting the Covid-19 X-ray images | |
| – This approach employed both SMA and WOA together to unify their advantages for overcoming the disadvantages of each one separately | |
| – Afterward, HSMA_WOA has validated 12 chest X-ray images and its outcomes were compared with those of a number of well-known optimization algorithms to see their efficacy | |
| – Finally, the experimental findings show the superiority of the HSMA_WOA over the others | |
| Disadvantages | |
| – Its performance for general test images has not been observed | |
| An equilibrium optimizer (EO) (Abdel-Basset et al. 2021) | Contributions |
| – In this paper, the equilibrium optimizer was adapted for the multilevel thresholding image segmentation problem by maximizing Kapur’s entropy to find the optimal threshold values for various threshold levels | |
| – It has been validated using a number of images and compared to some well-known optimization algorithms to appear its efficacy | |
| Disadvantages | |
| – Still suffers from falling inside local minima which prevents it from reaching the optimal threshold values | |
| Improved marine predators algorithm (IMPA) (Abdel-Basset et al. 2020) | Contributions |
| – Recently, a novel multilevel thresholding image segmentation approach has been proposed for segmenting the Covid-19 X-ray images | |
| – This approach was based on the marine predators algorithm improved by a ranking-based diversity reduction strategy to increase the exploitation capability of the standard marine predators algorithm | |
| – The experimental outcomes proved the superiority of this improved one in terms of PSNR, SSIM, standard deviation, fitness values, and UQI | |
| Disadvantages | |
| – A little expensive in terms of the computational cost compared to the standard MPA and some of the rival algorithms | |
| Antlion optimization (ALO) and multiverse optimization (MVO) algorithms (Chouksey et al. 2020) | Contributions |
| – In this paper, both ALO and MVO have been proposed for overcoming the multilevel thresholding image segmentation problem by maximizing both Kapur’s entropy and the Otsu method | |
| – Those two algorithms were compared with other evolutionary methods in terms of PSNR, SSIM, feature similarity index (FSIM), standard deviation, stability analysis, and fitness values. The experimental results showed that MVO is faster and better than the compared methods | |
| Disadvantages | |
| – Its performance for threshold levels higher than 5 is not known and hence not preferred for the images that have threshold levels higher than that | |
| An improved Bloch quantum artificial bee colony algorithm (ABC) (Huo et al. 2020) | Contributions |
| – The ABC has been improved by the quantum Bloch spherical coordinates of the qubit for reaching better outcomes within a small number of iterations when solving the multilevel thresholding image segmentation problem | |
| – The experimental outcomes show the superiority of the proposed algorithm | |
| Disadvantages | |
| – Low convergence speed | |
| – Falling into local minima | |
| Coyote optimization algorithm (COA)(Moses 2020) | Contributions |
| – In this paper, the COA was adapted to tackle the ISP | |
| – The experimental outcomes showed the superiority of the COA in terms of convergence speed, objective values, and image quality | |
| Disadvantages | |
| – Moves slowly to the near-optimal solution and this will make it consume several function evaluations | |
| Crow search algorithm (CSA) (Moses et al. 2019) | Contributions |
| – Those authors proposed the CSA with the Otsu method as an objective function for selecting the optimal threshold values | |
| – The CSA proved its superiority over the improved particle swarm optimization (PSO), firefly algorithm (FFA), and also the fuzzy version of FA in terms of the quality of the segmented image, and the objective values | |
| Disadvantages | |
| – Low convergence speed | |
| – Not observed for threshold levels greater than 5 | |
| Modified water wave optimization (MWWO) algorithm (Yan et al. 2020) | Contributions |
| – In this paper, the water wave optimization algorithm was modified by the opposition-based learning strategy and ranking-based mutation strategy to find the optimal values for the underwater image segmentation problem | |
| – The opposition-based learning was used to increase the diversity of the individuals to avoid being stuck into local minima and reach better outcomes. While the ranking-based mutation operator was used to improve the selection probability | |
| – The experimental results showed the superiority of MWWO in terms of the segmented images and the objective values over the other compared algorithms | |
| Disadvantages | |
| – Not compared with the recently-published algorithms where the latest compared algorithm was published in 2017 | |
| Modified Red Deer Algorithm (MRDA) (De et al. 2020) | Contributions |
| – The red deer algorithm modified by a few adaptive approaches to improve its efficacy has been proposed in this research for tackling the image segmentation problem | |
| – This algorithm was compared with the standard one and genetic algorithm over a set of real-life test images and could prove its efficacy in terms of fitness value, convergence speed, and standard deviation | |
| Disadvantages | |
| – Not investigated using several test images to check its stability, in addition to using a huge number of iteration up to 1000 which notifies its low convergence speed in the right direction of the near-optimal solution | |
| Modified hybrid bat algorithm (Yue and Zhang 2020) | Contributions |
| – Recently, the bat algorithm has been modified by a genetic crossover operator and a smart inertia weight (SGA-BA) to enhance its performance for maximizing the Otsu method to estimate the optimal thresholds of a set of images | |
| Disadvantages | |
| – Consuming computational cost higher than the other compared algorithm | |
| Improved flower pollination optimizer (IFPA) (Li and Tan 2019) | Contributions |
| – In this paper, the authors improved the flower pollination algorithm for optimizing the Tsallis entropy as an objective function to find the optimal thresholds that separate similar regions within an image | |
| – The experimental results show the superiority of this improved one compared to those three algorithms | |
| Grey Wolf Optimizer (GWO)(Khairuzzaman and Chaudhury 2017) | Contributions |
| – The GWO has been proposed for finding the optimal thresholds to separate similar regions within an image. This algorithm used Kapur’s entropy and Otsu method as objective functions to find those optimal thresholds | |
| – The experimental results show that GWO could be superior in terms of the quality of segmented images and stability and speed | |
| Disadvantages | |
| – Using the intensity of the image to perform the segmentation process | |
| – Not adequate for the images having intensity inhomogeneity problem |