A multilevel thresholding algorithm using LebTLBO for image segmentation

Singh, Simrandeep; Mittal, Nitin; Singh, Harbinder

doi:10.1007/s00521-020-04989-2

Cited by 43 publications

(15 citation statements)

References 37 publications

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“…With the development of machine learning theory and methods, many optimization algorithms have emerged and been successfully applied (e.g. Singh et al., 2020 , 2021 ; Mittal et al., 2021 ). Among these optimization algorithms, genetic algorithm (GA) is very suitable for solving nonlinear problems according to the characteristics of population searching strategy, information interchange and searching independent on gradient information, and it has been applied to many fields such as machine learning, function optimization, pattern recognition and so on (e.g.…”

Section: Introductionmentioning

confidence: 99%

Fault diagnosis for cooling dehumidifier based on fuzzy classifier optimized by adaptive genetic algorithm

Gao

Wang

2022

Heliyon

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Section: Introductionmentioning

confidence: 99%

Fault diagnosis for cooling dehumidifier based on fuzzy classifier optimized by adaptive genetic algorithm

Gao

Wang

2022

Heliyon

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“…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).…”

Section: Literature Reviewmentioning

confidence: 99%

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

Houssein

Emam

Ali

2021

Neural Comput & Applic

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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.

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“…In order to test the efficiency of the proposed TSNMRA, CEC 2019 benchmark problems [19] and real multilevel image thresholding problem [20] have been used. The segmentation of digital images is an open problem that has increasingly attracted the attention of researchers during the last years.…”

Section: Introductionmentioning

confidence: 99%

A Novel Hybrid Tunicate Swarm Naked Mole-Rat Algorithm for Image Segmentation and Numerical Optimization

Singh¹,

Mittal²,

Singh³

et al. 2022

Computers, Materials &Amp; Continua

Self Cite

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This paper provides a new optimization algorithm named as tunicate swarm naked mole-rat algorithm (TSNMRA) which uses hybridization concept of tunicate swarm algorithm (TSA) and naked mole-rat algorithm (NMRA). This newly developed algorithm uses the characteristics of both algorithms (TSA and NMRA) and enhance the exploration abilities of NMRA. Apart from the hybridization concept, important parameter of NMRA such as mating factor is made to be self-adaptive with the help of simulated annealing (sa) mutation operator and there is no need to define its value manually. For evaluating the working capabilities of proposed TSNMRA, it is tested for 100-digit challenge (CEC 2019) test problems and real multi-level image segmentation problem. From the results obtained for CEC 2019 test problems, it can be seen that proposed TSNMRA performs well as compared to original TSA and NMRA. In case of image segmentation problem, comparison of TSNMRA is performed with multi-threshold electro magnetism-like optimization (MTEMO), particle swarm optimization (PSO), genetic algorithm (GA), bacterial foraging (BF) and found superior results for TSNMRA.

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A multilevel thresholding algorithm using LebTLBO for image segmentation

Cited by 43 publications

References 37 publications

Fault diagnosis for cooling dehumidifier based on fuzzy classifier optimized by adaptive genetic algorithm

Fault diagnosis for cooling dehumidifier based on fuzzy classifier optimized by adaptive genetic algorithm

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

A Novel Hybrid Tunicate Swarm Naked Mole-Rat Algorithm for Image Segmentation and Numerical Optimization

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