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Multilevel thresholding based image segmentation using new multistage hybrid optimization algorithm

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Abstract

Thresholding is one of the highly accepted methods for image segmentation because of its simplicity in nature. The selection of optimal threshold values in threshold-based image segmentation is a tricky job. In this work, Kapur’s entropy is used to solve the optimal threshold selection problem and a multistage hybrid nature-inspired optimization algorithm is used to get the best possible parameters for this objective function. The proposed method has three stages namely: primary stage, booster stage and final stage. Particle swarm optimization (PSO), artificial bee colony optimization (ABC) and ant colony optimization (ACO) used at these stages. In this proposed work various benchmarked images have been used for experimentation purpose. The proposed method has been assessed and performance is compared with well-known metaheuristic optimization like PSO, ABC, ACO, classical Otsu thresholding method and modified bacterial foraging optimization qualitatively and quantitatively. Peak signal to noise ratio and Structure Similarity Index are used for qualitative assessment. Wilcoxon p value test, ANOVA test and box plots are used for statistical analysis. The experimental results showed that the proposed method performed better in terms of quality and consistency.

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References

  • Amarjeet JK, Chhabra JK (2018) TA-ABC: two-archive artificial bee colony for multi-objective software module clustering problem. J Intell Syst 27(4):619–641

    Article  Google Scholar 

  • Cuevas E (2013) Block-matching algorithm based on harmony search optimization for motion estimation. Appl Intel 39(1):165–183

    Article  Google Scholar 

  • Dorigo M, Blum C (2005) Ant colony optimization theory: a survey. Theor Comput Sci 344(2–3):243–278

    Article  MathSciNet  Google Scholar 

  • Gao H et al (2010) Multilevel thresholding for image segmentation through an improved quantum-behaved particle swarm algorithm. IEEE Trans Instrum Meas 59(4):934–946

    Article  Google Scholar 

  • Ghamisi P et al (2014) Multilevel image segmentation based on fractional-order darwinian particle swarm optimization. IEEE Trans Geosci Remote Sens 52(5):2382–2394

    Article  Google Scholar 

  • Gonzalez RC, Woods RE (2008) Digital image processing, 3rd edn. PHI, New Delhi

    Google Scholar 

  • Horng MH (2011) Multilevel thresholding selection based on the artificial bee colony algorithm for image segmentation. Expert Syst Appl 38(11):13785–13791

    Google Scholar 

  • Horng MH, Liou RJ (2011) Multilevel minimum cross entropy threshold selection based on the firefly algorithm. Expert Syst Appl 38:14805–14811

    Article  Google Scholar 

  • Jiang Y et al (2017) A honey-bee-mating based algorithm for multilevel image segmentation using Bayesian theorem. Appl Soft Comput 52:1181–1190

    Article  Google Scholar 

  • Kang JG et al (2012) A new approach to simultaneous localization and map building with implicit model learning using neuro evolutionary optimization. Appl Intell 36(1):242–269

    Article  Google Scholar 

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

    Article  Google Scholar 

  • Karaboga D (2005) An idea based on honey bee swarm for numerical optimization. Technical Report TR06, Erciyes University, Engineering Faculty, Computer Engineering Department

  • Kayom A et al (2017) Moth-flame optimization algorithm based multilevel thresholding for image segmentation. Int J Appl Metaheuristic Comput 8(4):58–83

    Article  Google Scholar 

  • Kayom A et al (2019) Brain MR image multilevel thresholding by using particle swarm optimization, Otsu method and anisotropic diffusion. Int J Appl Metaheuristic Comput 10(3):91–106

    Article  Google Scholar 

  • Kennedy J, Eberhart RC (1995) Particle swarm optimization. In: Proceedings of IEEE international conference on neural networks, Perth, Australia, pp 1942–1948

  • Khan MW (2014) A survey: image segmentation techniques. Int J Future Comput Commun 3(2):89–93

    Article  Google Scholar 

  • Li L et al (2017) A quick artificial bee colony algorithm for image thresholding. Information 8(1):16

    Article  Google Scholar 

  • Liang Y, Wang L (2019) Applying genetic algorithm and ant colony optimization algorithm into marine investigation path planning model. Soft Comput. https://doi.org/10.1007/s00500-019-04414-4

    Article  Google Scholar 

  • Martin D et al (2001) A database of human segmented natural images and its application to evaluating segmentation algorithms and measuring ecological statistics. In: Proceedings of IEEE international conference on computer vision, Vancouver, Canada, pp 416–424

  • Oliva D et al (2014) A multilevel thresholding algorithm using electromagnetism optimization. Neurocomputing 139:357–381

    Article  Google Scholar 

  • Oliva D et al (2017) Cross entropy based thresholding for magnetic resonance brain images using crow search algorithm. Expert Syst Appl 79:164–180

    Article  Google Scholar 

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

    Article  Google Scholar 

  • Pare S et al (2015) Satellite image segmentation based on different objective functions using genetic algorithm: a comparative study. In: Proceedings of IEEE international conference on digital signal processing (DSP), Singapore, pp 730–734

  • Prajapati A, Chhabra JK (2018) A particle swarm optimization-based heuristic for software module clustering problem. Arab J Sci Eng 43:7083–7094

    Article  Google Scholar 

  • Rathee A, Chhabra JK (2019) Mining reusable software components from object-oriented source code using discrete PSO and modeling them as Java Beans. Inf Syst Front. https://doi.org/10.1007/s10796-019-09948-4

    Article  Google Scholar 

  • Resma KPB, Nair MS (2018) Multilevel thresholding for image segmentation using Krill Herd Optimization algorithm. J King Saud Univ Comput Inf Sci. https://doi.org/10.1016/j.jksuci.2018.04.007

    Article  Google Scholar 

  • Sezgin M, Sankur B (2004) Survey over image thresholding techniques and quantitative performance evaluation. J Electron Imaging 13(1):146–165

    Article  Google Scholar 

  • Sharma M, Chhabra JK (2019) Sustainable automatic data clustering using hybrid PSO algorithm with Mutation. Sustain Comput Inform Syst 23:144–157

    Google Scholar 

  • Tang K et al (2017) An improved multilevel thresholding approach based modified bacterial foraging optimization. Appl Intell 46(1):214–226

    Article  Google Scholar 

  • Tsai W (1985) Moment-preserving thresholding: a new approach. Comput Vis Graph Image Process 29:377–393

    Article  Google Scholar 

  • Vantaram SR, Saber E (2012) Survey of contemporary trends in color image segmentation. J Electron Imaging 21(4):040901–040928

    Article  Google Scholar 

  • Wang Z (2004) Image quality assessment: from error visibility to structural similarity. IEEE Trans Image Process 13(4):600–612

    Article  Google Scholar 

  • Wang S et al (2008) A novel image thresholding method based on Parzen window estimate. Pattern Recognit 41(1):117–129

    Article  Google Scholar 

  • Yang XS (2011) Metaheuristic optimization. Scholarpedia 6(8):11472

    Article  Google Scholar 

  • Yin PY (2007) Multilevel minimum cross entropy threshold selection based on particle swarm optimization. Appl Math Comput 184:503–512

    MathSciNet  MATH  Google Scholar 

  • Yin PY, Chen LH (1993) New method for multilevel thresholding using the symmetry and the duality of the histogram. J Electron Imaging 2:337–344

    Article  Google Scholar 

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Authors and Affiliations

  1. NIT Kurukshetra, Kurukshetra, Haryana, India

    Pankaj Upadhyay & Jitender Kumar Chhabra

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  1. Pankaj Upadhyay
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  2. Jitender Kumar Chhabra
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Correspondence to Pankaj Upadhyay.

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Upadhyay, P., Chhabra, J.K. Multilevel thresholding based image segmentation using new multistage hybrid optimization algorithm. J Ambient Intell Human Comput 12, 1081–1098 (2021). https://doi.org/10.1007/s12652-020-02143-3

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  • DOI: https://doi.org/10.1007/s12652-020-02143-3

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