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Hybrid marine predators algorithm for image segmentation: analysis and validations

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Abstract

Naturally, to analyze an image accurately, all the similar objects within it should be separated to pay attention to the most important object for reaching more details and hence achieving better accuracy. Therefore, multilevel thresholding is an indispensable image processing technique in the field of image segmentation and is employed widely to separate those similar objects. However, with increasing thresholds, the existing image segmentation techniques might suffer from exponentially-grown computational cost and low accuracy due to local optima shortage. Therefore, in this paper, a new image segmentation algorithm based on the improved marine predators algorithm (MPA) is proposed. MPA is improved using a strategy to find a number of the worst solutions within the population then tries to search for other better ones for those solutions by moving them gradually towards the best solutions to avoid accelerating to local optima and randomly within the search space based on a certain probability. In addition, this number of the worst solutions is increased with the iteration. This strategy is known as the linearly increased worst solutions improvement strategy (LIS). Also, we suggested that apply the ranking strategy based on a novel updating scheme, namely ranking-based updating strategy (RUS), on the solutions that could find better solutions in the last number iterations, perIter, in the hope of finding better solutions near it. RUS updates the particles/solutions which could not find better solutions than the best-local one in a number of consecutive iterations, with those that are generated based on a novel updating strategy. LIS is integrated with MPA to produce a new segmentation meta-heuristic algorithm abbreviated as MPALS. Also, MPALS and RUS are combined to tackle ISP in a strong variant abbreviated as HMPA for overcoming the image segmentation problem. The two proposed algorithms are validated on 14 test images and compared with seven state-of-the-arts meta-heuristic algorithms. The experimental results show the effectiveness of HMPA with increasing the threshold levels compared to the seven state-of-the-arts algorithms when segmenting an image, while their performance is roughly the same for the image with a small threshold level.

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References

  • Abd El Aziz M, Ewees AA, Hassanien AE (2017) Whale optimization algorithm and moth-flame optimization for multilevel thresholding image segmentation. Expert Syst Appl 83:242–256

    Google Scholar 

  • Abdel-Basset M, Manogaran G, El-Shahat D, Mirjalili S (2018) A hybrid whale optimization algorithm based on local search strategy for the permutation flow shop scheduling problem. Futur Gener Comput Syst 85:129–145

    Google Scholar 

  • Abdel-Basset M, Mohamed R, Elhoseny M, Chakrabortty RK, Ryan M (2020) A hybrid Covid-19 detection model using an improved marine predators algorithm and a ranking-based diversity reduction strategy. IEEE Access 8:79521–79540

    Google Scholar 

  • Abdel-Basset M, Mohamed R, Mirjalili S, Chakrabortty RK, Ryan MJ (2020) Solar photovoltaic parameter estimation using an improved equilibrium optimizer. Sol Energy 209:694–708

    Google Scholar 

  • Abdel-Basset M, Chang V, Mohamed R (2020) A novel equilibrium optimization algorithm for multi-thresholding image segmentation problems. Neural Comput Appl 1–34

  • Abdel-Basset M, Mohamed R, Mirjalili S, Chakrabortty RK, Ryan M (2021) An efficient marine predators algorithm for solving multi-objective optimization problems: analysis and validations. IEEE Access 9:42817–42844

    Google Scholar 

  • Abouhawwash M, Alessio AM (2021) Multi-objective evolutionary algorithm for pet image reconstruction: Concept. IEEE Trans Med Imaging 40(8):2142–2151

    Google Scholar 

  • Agrawal S, Panda R, Bhuyan S, Panigrahi BK (2013) Tsallis entropy based optimal multilevel thresholding using cuckoo search algorithm. Swarm Evol Comput 11:16–30

    Google Scholar 

  • Aksac A, Ozyer T, Alhajj R (2017) Complex networks driven salient region detection based on superpixel segmentation. Pattern Recognit 66:268–279

    Google Scholar 

  • Arora S, Acharya J, Verma A, Panigrahi PK (2008) Multilevel thresholding for image segmentation through a fast statistical recursive algorithm. Pattern Recognit Lett 29(2):119–125

    Google Scholar 

  • Bao X, Jia H, Lang C (2019) A novel hybrid Harris Hawks optimization for color image multilevel thresholding segmentation. IEEE Access 7:76529–76546

    Google Scholar 

  • Barman R, Ehrmann M, Clematide S, Oliveira SA, Kaplan F (2020) Combining visual and textual features for semantic segmentation of historical newspapers. arXiv preprint arXiv:2002.06144

  • Bhandari AK, Kumar A, Singh GK (2015) Modified artificial bee colony based computationally efficient multilevel thresholding for satellite image segmentation using Kapur’s, Otsu and Tsallis functions. Expert Syst Appl 42(3):1573–1601

  • Chakraborty F, Nandi D, Roy PK (2019) Oppositional symbiotic organisms search optimization for multilevel thresholding of color image. Appl Soft Comput 82:105577

    Google Scholar 

  • Chen K, Zhou Y, Zhang Z, Dai M, Chao Y, Shi J (2016) Multilevel image segmentation based on an improved firefly algorithm. Math Probl Eng 2016

  • Chouksey M, Jha RK, Sharma R (2020) A fast technique for image segmentation based on two meta-heuristic algorithms. Multimedia Tools Appl 1–53

  • Cuevas E, Fausto F, González A (2020) Locust search algorithm applied to multi-threshold segmentation. In: New advancements in swarm algorithms: operators and applications. Springer, pp 211–240

  • Di Martino F, Sessa S (2020) PSO image thresholding on images compressed via fuzzy transforms. Inf Sci 506:308–324

    MathSciNet  MATH  Google Scholar 

  • Diab AAZ, Tolba MA, El-Magd AGA, Zaky MM, El-Rifaie AM (2020) Fuel cell parameters estimation via marine predators and political optimizers. IEEE Access 8:166998–167018

    Google Scholar 

  • Durmus A (2021) The concentric elliptical antenna array patterns synthesis using marine predators algorithm. Arab J Sci Eng 1–11

  • Elsayed SM, Sarker RA, Essam DL (2014) A new genetic algorithm for solving optimization problems. Eng Appl Artif Intell 27:57–69

    Google Scholar 

  • Elsayed AM, Shaheen AM, Alharthi MM, Ghoneim SS, El-Sehiemy RA (2021) Adequate operation of hybrid AC/MT-HVDC power systems using an improved multi-objective marine predators optimizer. IEEE Access 9:51065–51087

    Google Scholar 

  • Erdmann H, Wachs-Lopes G, Gallao C, Ribeiro M, Rodrigues P (2015) A study of a firefly meta-heuristics for multithreshold image segmentation. In: Developments in medical image processing and computational vision. Springer, pp 279–295

  • Faramarzi A, Heidarinejad M, Mirjalili S, Gandomi AH (2020) Marine predators algorithm: a nature-inspired metaheuristic. Expert Syst Appl 152:113377

    Google Scholar 

  • Ghoneimy M, Hassan HA, Nabil E (2021) A new hybrid clustering method of binary differential evolution and marine predators algorithm for multi-omics datasets

  • Guo C, Li H (2007) Multilevel thresholding method for image segmentation based on an adaptive particle swarm optimization algorithm. In: Australasian joint conference on artificial intelligence. Springer, pp 654–658

  • Han J, Yang C, Zhou X, Gui W (2017) A new multi-threshold image segmentation approach using state transition algorithm. Appl Math Model 44:588–601

    MathSciNet  MATH  Google Scholar 

  • Hassanzadeh T, Essam D, Sarker R (2020) An evolutionary denseres deep convolutional neural network for medical image segmentation. IEEE Access, vol. 8, pp 212 298–212 314

  • Hore A, Ziou D (2010) Image quality metrics: PSNR vs. SSIM. In: 20th international conference on pattern recognition. IEEE 2010:2366–2369

  • Horng M-H (2010) Multilevel minimum cross entropy threshold selection based on the honey bee mating optimization. Expert Syst Appl 37(6):4580–4592

    Google Scholar 

  • Houssein EH, Helmy BE-D, Elngar AA, Abdelminaam DS, Shaban H (2021) An improved tunicate swarm algorithm for global optimization and image segmentation. IEEE Access 9:56066–56092

    Google Scholar 

  • Huo F, Sun X, Ren W (2020) Multilevel image threshold segmentation using an improved bloch quantum artificial bee colony algorithm. Multimedia Tools Appl 79(3):2447–2471

    Google Scholar 

  • Kandhway P, Bhandari AK (2019) Spatial context cross entropy function based multilevel image segmentation using multi-verse optimizer. Multimedia Tools Appl 78(16):22613–22641

    Google Scholar 

  • Kapur JN, Sahoo PK, Wong AK (1985) A new method for gray-level picture thresholding using the entropy of the histogram. Comput Vis Graph Image Process 29(3):273–285

    Google Scholar 

  • Karydas CG (2020) Optimization of multi-scale segmentation of satellite imagery using fractal geometry. Int J Remote Sens 41(8):2905–2933

    Google Scholar 

  • Kaveh A, Talatahari S (2010) An improved ant colony optimization for constrained engineering design problems. Eng Comput

  • Kuruvilla J, Sukumaran D, Sankar A, Joy SP (2016) A review on image processing and image segmentation. In: International conference on data mining and advanced computing (SAPIENCE). IEEE 2016:198–203

  • Lam F, Longnecker M (1983) A modified wilcoxon rank sum test for paired data. Biometrika 70(2):510–513

    MathSciNet  Google Scholar 

  • Li S, Chen H, Wang M, Heidari AA, Mirjalili S (2020) Slime mould algorithm: a new method for stochastic optimization. Futur Gener Comput Syst 111:300–323

    Google Scholar 

  • Li W, Lin Q, Wang K, Cai K (2021) Improving medical image fusion method using fuzzy entropy and nonsubsampling contourlet transform. Int J Imaging Syst Technol 31(1):204–214

    Google Scholar 

  • Liu X, Yang D (2021) Color constancy computation for dyed fabrics via improved marine predators algorithm optimized random vector functional-link network. Color Res Appl

  • Liu Y, Mu C, Kou W, Liu J (2015) Modified particle swarm optimization-based multilevel thresholding for image segmentation. Soft Comput 19(5):1311–1327

    Google Scholar 

  • Ma L, Cheng S, Shi Y (2020) Enhancing learning efficiency of brain storm optimization via orthogonal learning design. IEEE Trans Syst Man Cybern Syst. https://doi.org/10.1109/TSMC.2020.2963943

    Article  Google Scholar 

  • Ma L, Huang M, Yang S, Wang R, Wang X (2021) An adaptive localized decision variable analysis approach to large-scale multiobjective and many-objective optimization. IEEE Trans Cybern. https://doi.org/10.1109/TCYB.2020.3041212

    Article  Google Scholar 

  • Mahajan S, Mittal N, Pandit AK (2021) Image segmentation using multilevel thresholding based on type ii fuzzy entropy and marine predators algorithm. Multimedia Tools Appl 80(13):19335–19359

    Google Scholar 

  • Maitra M, Chatterjee A (2008) A hybrid cooperative-comprehensive learning based PSO algorithm for image segmentation using multilevel thresholding. Expert Syst Appl 34(2):1341–1350

    Google Scholar 

  • Mirjalili S, Lewis A (2016) The whale optimization algorithm. Adv Eng Softw 95:51–67

    Google Scholar 

  • Mittal M, Arora M, Pandey T, Goyal LM (2020) Image segmentation using deep learning techniques in medical images. In: Advancement of machine intelligence in interactive medical image analysis. Springer, pp 41–63

  • Mokhtari SY, Kimour MT (2019) A novel improved bat algorithm based image multi-thresholding. Int J Electr Eng Inf 11(2)

  • Naji Alwerfali HS, Al-qaness MA, Abd Elaziz M, Ewees AA, Oliva D, Lu S (2020) Multi-level image thresholding based on modified spherical search optimizer and fuzzy entropy. Entropy 22(3):328

    MathSciNet  Google Scholar 

  • Naoum A, Nothman J, Curran J (2019) Article segmentation in digitised newspapers with a 2d markov model. In: 2019 international conference on document analysis and recognition (ICDAR). IEEE, pp 1007–1014

  • Narayanan BN, Hardie RC, Kebede TM, Sprague MJ (2019) Optimized feature selection-based clustering approach for computer-aided detection of lung nodules in different modalities. Pattern Anal Appl 22(2):559–571

    MathSciNet  Google Scholar 

  • Oliva D, Cuevas E, Pajares G, Zaldivar D, Osuna V (2014) A multilevel thresholding algorithm using electromagnetism optimization. Neurocomputing 139:357–381

    Google Scholar 

  • Oliva D, Hinojosa S, Cuevas E, Pajares G, Avalos O, Gálvez J (2017) Cross entropy based thresholding for magnetic resonance brain images using crow search algorithm. Expert Syst Appl 79:164–180

    Google Scholar 

  • Otsu N (1979) A threshold selection method from gray-level histograms. IEEE Trans Syst Man Cybern 9(1):62–66

    Google Scholar 

  • Panagant N, Yıldız M, Pholdee N, Yıldız AR, Bureerat S, Sait SM (2021) A novel hybrid marine predators-Nelder-Mead optimization algorithm for the optimal design of engineering problems. Mater Test 63(5):453–457

    Google Scholar 

  • Prathusha P, Jyothi S (2018) A novel edge detection algorithm for fast and efficient image segmentation. In: Data engineering and intelligent computing. Springer, pp 283–291

  • Ramezani M, Bahmanyar D, Razmjooy N (2021) A new improved model of marine predator algorithm for optimization problems. Arab J Sci Eng 1–24

  • Riad N, Anis W, Elkassas A, Hassan AE-W (2021) Three-phase multilevel inverter using selective harmonic elimination with marine predator algorithm. Electronics 10(4):374

    Google Scholar 

  • Ridha HM (2020) Parameters extraction of single and double diodes photovoltaic models using marine predators algorithm and lambert W function. Sol Energy 209:674–693

    Google Scholar 

  • Sanyal N, Chatterjee A, Munshi S (2011) An adaptive bacterial foraging algorithm for fuzzy entropy based image segmentation. Expert Syst Appl 38(12):15489–15498

    Google Scholar 

  • Saremi S, Mirjalili SZ, Mirjalili SM (2015) Evolutionary population dynamics and grey wolf optimizer. Neural Comput Appl 26(5):1257–1263

    Google Scholar 

  • Sayed GI, Hassanien AE, Azar AT (2019) Feature selection via a novel chaotic crow search algorithm. Neural Comput Appl 31(1):171–188

    Google Scholar 

  • Shahabi F, Pourahangarian F, Beheshti H (2019) A multilevel image thresholding approach based on crow search algorithm and Otsu method

  • Shaheen MA, Yousri D, Fathy A, Hasanien HM, Alkuhayli A, Muyeen S (2020) A novel application of improved marine predators algorithm and particle swarm optimization for solving the ORPD problem. Energies 13(21):5679

    Google Scholar 

  • Shaheen AM, Elsayed AM, El-Sehiemy RA, Kamel S, Ghoneim SS (2021) A modified marine predators optimization algorithm for simultaneous network reconfiguration and distributed generator allocation in distribution systems under different loading conditions. Eng Optim 1–22

  • Soliman MA, Hasanien HM, Alkuhayli A (2020) Marine predators algorithm for parameters identification of triple-diode photovoltaic models. IEEE Access 8:155832–155842

    Google Scholar 

  • Sultana F, Sufian A, Dutta P (2020) Evolution of image segmentation using deep convolutional neural network: a survey. Knowl-Based Syst 201:106062

    Google Scholar 

  • Swief RA, Hassan NM, Hasanien HM, Abdelaziz AY, Kamh MZ (2021) Multi-regional optimal power flow using marine predators algorithm considering load and generation variability. IEEE Access

  • Tang K, Xiao X, Wu J, Yang J, Luo L (2017) An improved multilevel thresholding approach based modified bacterial foraging optimization. Appl Intell 46(1):214–226

    Google Scholar 

  • University of California. www2.eecs.berkeley.edu/research/projects/cs/vision/grouping/resources.html

  • Wang R, Zhou Y, Zhao C, Wu H (2015) A hybrid flower pollination algorithm based modified randomized location for multi-threshold medical image segmentation. Bio-Med Mater Eng 26(s1):S1345–S1351

    Google Scholar 

  • Wang X, Wang X, Wilkes DM (2020) An efficient image segmentation algorithm for object recognition using spectral clustering. In: Machine learning-based natural scene recognition for mobile robot localization in an unknown environment. Springer, pp 215–234

  • Wang Z, Wang Q, Zhang Z, Razmjooy N (2021) A new configuration of autonomous CHP system based on improved version of marine predators algorithm: a case study. Int Trans Electr Energy Syst 31(4):e12806

    Google Scholar 

  • Xiong L, Tang G, Chen Y-C, Hu Y-X, Chen R-S (2020) Color disease spot image segmentation algorithm based on chaotic particle swarm optimization and FCM. J Supercomput 1–15

  • Xu L, Jia H, Lang C, Peng X, Sun K (2019) A novel method for multilevel color image segmentation based on dragonfly algorithm and differential evolution. IEEE Access 7:19502–19538

    Google Scholar 

  • Yan Z, Zhang J, Tang J (2020) Modified water wave optimization algorithm for underwater multilevel thresholding image segmentation. Multimedia Tools Appl 1–34

  • Yao X, Li Z, Liu L, Cheng X (2019) Multi-threshold image segmentation based on improved grey wolf optimization algorithm. In: IOP conference series: earth and environmental science, vol. 252, no. 4. IOP Publishing, p 042105

  • Yang X-S (2012) Flower pollination algorithm for global optimization. In: International conference on unconventional computing and natural computation. Springer, pp 240–249

  • Yu G, Meng Z, Ma H, Liu L (2021) An adaptive marine predators algorithm for optimizing a hybrid PV/DG/battery system for a remote area in China. Energy Rep 7:398–412

    Google Scholar 

  • Zhang Z, Wu C, Coleman S, Kerr D (2020) Dense-inception u-net for medical image segmentation. Comput Methods Programs Biomed 192:105395

    Google Scholar 

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Authors and Affiliations

  1. Zagazig Univesitry, Shaibet an Nakareyah, Zagazig 2, Ash Sharqia Governorate, 44519, Egypt

    Mohamed Abdel-Basset & Reda Mohamed

  2. Department of Mathematics Faculty of Science, Mansoura University, Mansoura, 35516, Egypt

    Mohamed Abouhawwash

  3. Department of Computational Mathematics, Science, and Engineering (CMSE), Michigan State University, East Lansing, MI, 48824, USA

    Mohamed Abouhawwash

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  1. Mohamed Abdel-Basset
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Abdel-Basset, M., Mohamed, R. & Abouhawwash, M. Hybrid marine predators algorithm for image segmentation: analysis and validations. Artif Intell Rev 55, 3315–3367 (2022). https://doi.org/10.1007/s10462-021-10086-0

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