Cauchy with whale optimizer based eagle strategy for multi-level color hematology image segmentation

Ray, Swarnajit; Das, Arunita; Dhal, Krishna Gopal; Gálvez, Jorge; Naskar, Prabir Kumar

doi:10.1007/s00521-020-05368-7

Cited by 26 publications

(9 citation statements)

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“…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 ) Gray Scale Images Proposed method is compared with k-means, W-k-means, MW-k-means, EW-k-means (Shannon) NMI and ARI The result shows that the proposed measure satisfies the major axiomatic properties of entropy. The application of t-entropy in the context of Power k-means clustering and sparse signal recovery is also some possible avenues for future research 15 Eagle Strategy—Whale Optimization Algorithm (ES-WOA) Kapur’s, Fuzzy, Tsallis’, Otsu’s thresholding and Cross entropy Ray et al ( 2021 ) Color Hematology Images: Medical Images Proposed method is compared with ES-DE, ES-FA, ES-PSO and WOA PSNR, FSIM and QILV The proposed ES-WOA (Tsallis) acquires the best threshold values generating quicker execution times for all the images considered to be tested. However, other types of medical images namely histology, MRI, CT, Mammogram could be used with the proposed method 16 Multilevel Thresholding Based on Fuzzy-Masi Entropy Fuzzy-Masi entropy He et al ( 2021 ) Standard Color Images Proposed method is compared with Masi and Tsalli’s entropy PSNR, FSIM and SSIM The results illustrate that fuzzy Masi entropy displays advanced quality and performance than other objective functions considered under the banner 17 Fuzzy Entropy Type II (FE-TII) combined with Marine Predators Algorithm (MPA) (FE-TII-MPA) Type-II Fuzzy Entropy Mahajan et al ( …”

Section: Recent Trends In Multi-level Thresholding Using Nature-inspi...mentioning

confidence: 99%

“…Kapur method (Ray et al 2021 ) considers the combination of a probability distribution function of a given histogram with the concept of entropy. The principal idea behind this method, aims to find the best threshold combination that maximizes the entropy.…”

Section: Recent Trends In Multi-level Thresholding Using Nature-inspi...mentioning

confidence: 99%

“…al. ( 2021 ) claimed that Tsallis entropy is better than Otsu, Cross, Fuzzy entropy, Kapur’s entropy for proper thresholding of color pathology images. Whereas, Suresh et.…”

Section: Recent Trends In Multi-level Thresholding Using Nature-inspi...mentioning

confidence: 99%

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

2022

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Multilevel Thresholding (MLT) is considered as a significant and imperative research field in image segmentation that can efficiently resolve difficulties aroused while analyzing the segmented regions of multifaceted images with complicated nonlinear conditions. MLT being a simple exponential combinatorial optimization problem is commonly phrased by means of a sophisticated objective function requirement that can only be addressed by nondeterministic approaches. Consequently, researchers are engaging Nature-Inspired Optimization Algorithms (NIOA) as an alternate methodology that can be widely employed for resolving problems related to MLT. This paper delivers an acquainted review related to novel NIOA shaped lately in last three years (2019–2021) highlighting and exploring the major challenges encountered during the development of image multi-thresholding models based on NIOA.

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Section: Recent Trends In Multi-level Thresholding Using Nature-inspi...mentioning

confidence: 99%

Section: Recent Trends In Multi-level Thresholding Using Nature-inspi...mentioning

confidence: 99%

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

2022

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“…In recent years, swarm-based intelligent optimization algorithms have been favored by many researchers because of their simplicity and high efficiency [ 1 ]. Swarm intelligence algorithms can effectively solve many complex optimization problems in the field of engineering, and are mainly used in network optimization [ 2 ], feature selection [ 3 ], image processing [ 4 ], automatic control [ 5 ], and other fields. In recent years, swarm intelligence optimization algorithms have been proposed, including butterfly optimization algorithm (BOA) [ 6 ], whale optimization algorithm (WOA) [ 7 ], sine cosine algorithm (SCA) [ 8 ], sparrow search algorithm (SSA) [ 9 ], marine predator algorithm (MPA) [ 10 ], African vultures optimization algorithm (AVOA) [ 11 ], manta ray foraging optimization (MRFO) algorithm [ 12 ], and so on.…”

Section: Introductionmentioning

confidence: 99%

Application of Improved Manta Ray Foraging Optimization Algorithm in Coverage Optimization of Wireless Sensor Networks

Fang

Wang

2022

Computational Intelligence and Neuroscience

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For the shortcomings of the manta ray foraging optimization (MRFO) algorithm, like slow convergence speed and difficult to escape from the local optimum, an improved manta ray foraging algorithm based on Latin hypercube sampling and group learning is proposed. Firstly, the Latin hypercube sampling (LHS) method is introduced to initialize the population. It divides the search space evenly so that the initial population covers the whole search space to maintain the diversity of the initial population. Secondly, in the exploration stage of cyclone foraging, the Levy flight strategy is introduced to avoid premature convergence. Before the somersault foraging stage, the adaptive t-distribution mutation operator is introduced to update the population to increase the diversity of the population and avoid falling into the local optimum. Finally, for the updated population, it is divided into leader group and follower group according to fitness. The follower group learns from the leader group, and the leader group learns from each other through differential evolution to further improve the population quality and search accuracy. 15 standard test functions are selected for comparative tests in low and high dimensions. The test results show that the improved algorithm can effectively improve the convergence speed and optimization accuracy of the original algorithm. Moreover, the improved algorithm is applied to wireless sensor network (WSN) coverage optimization. The experimental results show that the improved algorithm increases the network coverage by about 3% compared with the original algorithm, and makes the optimized node distribution more reasonable.

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“…On the other hand, the development and application of vision systems have accelerated in the recent era [31], [32], [33], [34]. Image processing without a vision system is illadvised, and a proper pre-processing improves the accuracy of the results.…”

Section: Introductionmentioning

confidence: 99%

An Improved Tunicate Swarm Algorithm for Global Optimization and Image Segmentation

et al. 2021

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This study integrates a tunicate swarm algorithm (TSA) with a local escaping operator (LEO) for overcoming the weaknesses of the original TSA. The LEO strategy in TSA-LEO prevents searching deflation in TSA and improves the convergence rate and local search efficiency of swarm agents. The efficiency of the proposed TSA-LEO was verified on the CEC'2017 test suite, and its performance was compared with seven metaheuristic algorithms (MAs). The comparisons revealed that LEO significantly helps TSA by improving the quality of its solutions and accelerating the convergence rate. TSA-LEO was further tested on a real-world problem, namely, segmentation based on the objective functions of Otsu and Kapur. A set of well-known evaluation metrics was used to validate the performance and segmentation quality of the proposed TSA-LEO. The proposed TSA-LEO outperforms other MA algorithms in terms of fitness, peak signal-to-noise ratio, structural similarity, feature similarity, and segmentation findings. INDEX TERMSMetaheuristic algorithms; Tunicate swarm algorithm (TSA); Local escaping operator (LEO); Multilevel thresholding; Image segmentation; Kapur's entropy and Otsu method.

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Cauchy with whale optimizer based eagle strategy for multi-level color hematology image segmentation

Cited by 26 publications

References 58 publications

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

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

Application of Improved Manta Ray Foraging Optimization Algorithm in Coverage Optimization of Wireless Sensor Networks

An Improved Tunicate Swarm Algorithm for Global Optimization and Image Segmentation

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