Image segmentation using multilevel thresholding based on type II fuzzy entropy and marine predators algorithm

Mahajan, Shubham; Mittal, Nitin; Pandit, Amit Kant

doi:10.1007/s11042-021-10641-5

Cited by 45 publications

(23 citation statements)

References 36 publications

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“…Natural images with diverse histogram distributions are used to test the suggested method. The proposed multilevel thresholding utilizing ASSA is compared to other evolutionary algorithms like PSO, PPA, PFA, DE [ 3 – 10 ], and HPFPPA-D [ 32 ] on ten benchmark images with varied attributes and complexities [ 40 ]. The complete step-by-step overview and working of the proposed model are represented in Figure 1 .…”

Section: Resultsmentioning

confidence: 99%

“…EAs are methods that work on optimization of the research problem, e.g., differential evolution (DE) [ 1 ], genetic algorithm (GA) [ 2 ], and ant lion optimizer (ALO) [ 3 ]. SI is dependent on the swarming nature of various species, e.g., dragonfly algorithm (DA) [ 4 ], firefly algorithm (FA) [ 5 ], gray wolf optimization (GWO), bat algorithm (BA) [ 6 , 7 ], and particle swarm optimization (PSO) [ 8 – 10 ].…”

Section: Introductionmentioning

confidence: 99%

“…In this article, a novel technique of ASSA along with thresholding methods is proposed for image segmentation, which is an area of research with high accuracy in segmentation. It is practically validated by testing the accuracy of outputs and computational time taken by many other existing, state-of-the-art algorithms like GA [ 2 ], PSO [ 8 ], FPA [ 5 ], BA [ 6 , 7 ], CS [ 9 ], DE [ 1 ], and MPA [ 10 ].…”

Section: Introductionmentioning

confidence: 99%

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An Efficient Adaptive Salp Swarm Algorithm Using Type II Fuzzy Entropy for Multilevel Thresholding Image Segmentation

Mahajan

Mittal

Salgotra

et al. 2022

Computational and Mathematical Methods in Medicine

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Salp swarm algorithm (SSA) is an innovative contribution to smart swarm algorithms and has shown its utility in a wide range of research domains. While it is an efficient algorithm, it is noted that SSA suffers from several issues, including weak exploitation, convergence, and unstable exploitation and exploration. To overcome these, an improved SSA called as adaptive salp swarm algorithm (ASSA) was proposed. Thresholding is among the most effective image segmentation methods in which the objective function is described in relation of threshold values and their position in the histogram. Only if one threshold is assumed, a segmented image of two groups is obtained. But on other side, several groups in the output image are generated with multilevel thresholds. The methods proposed by authors previously were traditional measures to identify objective functions. However, the basic challenge with thresholding methods is defining the threshold numbers that the individual must choose. In this paper, ASSA, along with type II fuzzy entropy, is proposed. The technique presented is examined in context with multilevel image thresholding, specifically with ASSA. For this reason, the proposed method is tested using various images simultaneously with histograms. For evaluating the performance efficiency of the proposed method, the results are compared, and robustness is tested with the efficiency of the proposed method to multilevel segmentation of image; numerous images are utilized arbitrarily from datasets.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

An Efficient Adaptive Salp Swarm Algorithm Using Type II Fuzzy Entropy for Multilevel Thresholding Image Segmentation

Mahajan

Mittal

Salgotra

et al. 2022

Computational and Mathematical Methods in Medicine

Self Cite

View full text Add to dashboard Cite

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“…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 ( 2021 ) Standard Gray Scale Images Proposed method is compared with PFA, PPA, DE, PSO and HPFPPA-D PSNR, SSIM and MSE Results achieved by FE-TII-MPA are comparatively better than other algorithms taken into consideration for the purpose of comparison 18 Leader Harris Hawks Optimization (LHHO) Masi entropy Naik et al ( 2021 ) Standard Gray Scale Images Proposed method is compared with HHO, CS, PSO, FA, WDO, STOA and DE PSNR, FSIM and SSIM The proposed LHHO rapidly attains the optimum threshold values in regard to the iteration count 19 Attacking Manta Ray Foraging Optimization (AMRFO) Maximum 3D Tsallis entropy Jena et al ( 2021 ...…”

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

confidence: 99%

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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“…σ I,I S defines the covariance of I and I S . c 1 and c 2 are equal to 6.5025 and 58.52252, respectively [30].…”

Section: Performance Measuresmentioning

confidence: 99%

Modified Artificial Ecosystem-Based Optimization for Multilevel Thresholding Image Segmentation

et al. 2021

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Multilevel thresholding is one of the most effective image segmentation methods, due to its efficiency and easy implementation. This study presents a new multilevel thresholding method based on a modified artificial ecosystem-based optimization (AEO). The differential evolution (DE) is applied to overcome the shortcomings of the original AEO. The main idea of the proposed method, artificial ecosystem-based optimization differential evolution (AEODE), is to employ the operators of the DE as a local search of the AEO to improve the ecosystem of solutions. We used benchmark images to test the performance of the AEODE, and we compared it to several existing approaches. The proposed AEODE achieved a high performance when evaluated by the structural similarity index (SSIM), peak signal-to-noise ratio (PSNR), and fitness values. Moreover, the AEODE outperformed the basic version of the AEO concerning SSIM and PSNR by 78% and 82%, respectively, which reserves the best features for each of AEO and DE.

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Image segmentation using multilevel thresholding based on type II fuzzy entropy and marine predators algorithm

Cited by 45 publications

References 36 publications

An Efficient Adaptive Salp Swarm Algorithm Using Type II Fuzzy Entropy for Multilevel Thresholding Image Segmentation

An Efficient Adaptive Salp Swarm Algorithm Using Type II Fuzzy Entropy for Multilevel Thresholding Image Segmentation

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

Modified Artificial Ecosystem-Based Optimization for Multilevel Thresholding Image Segmentation

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