Table 1.
List of some algorithms that work for the problem of image segmentation
| Reference no. | Year | Algorithm | Technique |
|---|---|---|---|
| [62] | 2017 | MFO–WOA | Both MFO and WOA algorithms were used for multilevel thresholding segmentation. The proposed method used Otsu’s as the fitness function and tested both WOA and MFO using several images |
| [63] | 2020 | ABC–SCA | In this method, a hybrid of the ABC algorithm and the SCA algorithm was proposed for multilevel thresholding image segmentation. The SCA is employed as a local search for the ABC to boost its performance. This model obtains good performances compared to several existing meta-heuristics methods |
| [10] | 2020 | HHO | The HHO algorithm is used for image segmentation and applied the minimum cross-entropy as a fitness function. The performance of the algorithm has been tested in standard images and digital mammograms. The proposed method is verified based on other comparable optimizers and two machine learning algorithms (K-means and the Fuzzy IterAg) |
| [64] | 2015 | FFO | The FFO algorithm has been proposed to maximize Otsu’s variance to solve time-consuming and low-accuracy problems in multilevel thresholding image segmentation |
| [65] | 2020 | EO | The EO algorithm was used to find the optimal threshold value for a grayscale image and applied the Kapur entropy as a fitness function. The performance of this algorithm is compared with seven other algorithms |
| [66] | 2016 | CS | This paper introduced the comparative performance study of different objective functions using cuckoo search and other optimization algorithms to solve the color image segmentation problem using Otsu or Kapur’s method |
| [67] | 2018 | ABC | This method presented an Otsu segmentation method based on the ABC algorithm |
| [68] | 2020 | PSO | This technique was used to segment the color images |
| [32] | 2019 | WOA–GWO–PSO | This method used three meta-heuristics algorithms for multilevel thresholding image segmentation to maximize the Otsu method. It tested on 20 benchmark test images using six different thresholds |
| [69] | 2018 | Firefly algorithm (FA) | This is a technique for multilevel color image thresholding used the fuzzy entropy as a fitness function and enhanced the FA algorithm by Levy flight (LF) strategy |
| [70] | 2020 | PSO | This paper proposed a non-revisiting quantum-behaved PSO (NrQPSO) algorithm to find the optimal multilevel thresholds for gray-level images using Kapur’s entropy as an objective function |
| [71] | 2020 | Teaching learning based optimization algorithm (TLBO) | In this paper, LebTLBO was applied on ten standard test images and used the Otsu and Kapur’s entropy objective functions for image segmentation and compared with the MTEMO, GA, PSO, and BF algorithms for both Otsu and Kapur’s entropy methods. The results demonstrated that the LebTLBO outperforms the compared algorithms |
| [72] | 2020 | DE | This paper proposed a beta differential evolution (BDE)-based fast color image multilevel thresholding method using two objective functions (Kapur’s and Tsallis entropy). The efficiency of the proposed method is examined over existing multilevel thresholding methods such as artificial bee colony, particle swarm optimization, wind-driven optimization, and differential evolution |
| [73] | 2021 | Black Widow optimization algorithm (BWO) | The BWO algorithm used to find the best threshold value to solve the multilevel thresholding image segmentation and used both Otsu and Kapur methods as objective functions |
| [74] | 2019 | Elephant herding optimization (EHO) | The authors enhance the EHO algorithm by the OBL and dynamic Cauchy mutation (DCM) to solve the multilevel image thresholding problem for image segmentation by maximizes two objective functions: Kapur’s entropy and between-class variance |