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A novel multilevel thresholding algorithm based on quantum computing for abdominal CT liver images

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Abstract

Image segmentation is considered one of the important tasks for extracting useful information from an image. Multilevel thresholding image segmentation is one of the effective techniques for image segmentation. The computation time grows exponentially of traditional multilevel thresholding methods such as Kapur with the number of thresholds. Meta-heuristic algorithms based on swarm intelligence have proved their efficiency in solving the multilevel thresholding optimization problem. In this paper, a new hybrid algorithm based on quantum computing (QC) and optimal foraging algorithm (OFA) for multilevel image segmentation is presented. The main characteristic of the proposed quantum optimal foraging algorithm (QOFA) is the integration of quantum operators such as interference with the optimization process of OFA to get a proper balance between exploration and exploitation phases. The feasibility of the proposed multilevel image segmentation algorithm has been evaluated and tested on real abdominal CT liver images. All the results are analyzed qualitatively and quantitatively to evaluate the effectiveness and the robustness of the proposed algorithm. The experimental results revealed that the proposed algorithm is a very promising algorithm and it can provide better segmentation results compared with the standard OFA.

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References

  1. Zhu GY, Zhang WB (2017) Optimal foraging algorithm for global optimization. Appl Soft Comput 51:294–313

    Article  Google Scholar 

  2. Zhang WB, Zhu GY (2017) Drilling path optimization by optimal foraging algorithm. IEEE Trans Ind Inform 14:2847–2856

    Article  Google Scholar 

  3. Sharma D, Sharaff A (2019) Identifying spam patterns in sms using genetic programming approach. In: 2019 international conference on intelligent computing and control systems (ICCS), pp 396–400

  4. Sharaff A, Gupta H (2019) Extra-tree classifier with metaheuristics approach for email classification. In: Bhatia S, Tiwari S, Mishra K, Trivedi M (eds) Advances in computer communication and computational sciences. Springer, Singapore, pp 189–197

    Chapter  Google Scholar 

  5. Nayyar A, Le D, Nguyen N (2018) Advances in swarm intelligence for optimizing problems in computer science. Chapman & Hall, London

    Book  MATH  Google Scholar 

  6. Nayyar A, Nguyen N (2018) Introduction to swarm intelligence. In: Nayyar A, Le D-N, Nguyen NG (eds) Advances in swarm intelligence for optimizing problems in computer science. Chapman and Hall, London, pp 53–78

    Chapter  Google Scholar 

  7. Wei C, Wang G (2020) Hybrid annealing krill herd and quantum-behaved particle swarm optimization. Mathematics 8:1–22

    Article  Google Scholar 

  8. Dabba A, Tari A, Meftali S (2020) Hybridization of moth flame optimization algorithm and quantum computing for gene selection in microarray data. J Ambient Intell Humaniz Comput 1–20. https://doi.org/10.1007/s12652-020-02434-9

  9. Zhang X, Liu W, Chen J, Wang Y, Gao P, Lei Z, Ma X (2020) Quantum-based feature selection for multiclassification problem in complex systems with edge computing. Complexity 1–25:2020

    MATH  Google Scholar 

  10. Montanaro A (2016) Quantum algorithms: an overview. npj Quantum Inf 2:1–8

    Article  Google Scholar 

  11. Han K, Kim J (2004) Quantum-inspired evolutionary algorithms with a new termination criterion, hu gate, and two phase scheme. IEEE Trans Evol Comput 8(2):156–169

    Article  MathSciNet  Google Scholar 

  12. Hu C, Xia Y, Zhang J (2018) Adaptive operator quantum-behaved pigeon-inspired optimization algorithm with application to UAV path planning. Algorithms 12:1–13

    Article  MATH  Google Scholar 

  13. Wang P, Cheng K, Huang Y, Li B, Ye X, Chen X (2018) Multiscale quantum harmonic oscillator algorithm for multimodal optimization. Comput Intell Neurosci 1–12:2018

    Google Scholar 

  14. Sayed G, Darwish A, Hassanien A (2017) Quantum multiverse optimization algorithm for optimization problems. Neural Comput Appl 31:2763–2780

  15. Xin-gang Z, Ji L, Jin M, Ying Z (2020) An improved quantum particle swarm optimization algorithm for environmental economic dispatch. Expert Syst Appl 152:1–27

    Article  Google Scholar 

  16. Ramadan H, Lachqar C, Tairi H (2020) A survey of recent interactive image segmentation methods. Comput Vis Media 6:355–384

  17. Shahabi F, Poorahangaryan F, Edalatpanah S, Beheshti H (2020) A multilevel image thresholding approach based on crow search algorithm and Otsu method. Int J Comput Intell Appl 19(02):1–20

    Article  Google Scholar 

  18. El-Sayed M, Abdelmgeid A, Hussien M, Sennary H (2020) A multi-level threshold method for edge detection and segmentation based on entropy. Comput Mater Contin 63(1):1–16

    Google Scholar 

  19. Chakraborty F, Roy P, Nandi D (2020) Elephant herding optimization for multi-level image thresholding. Int J Appl Metaheuristic Comput 11(4):64–90

    Article  Google Scholar 

  20. Chakraborty F, Kumar Roy P, Nandi D (2020) Elephant herding optimization for multi-level image thresholding. Int J Appl Metaheuristic Comput 11(4):64–90

    Article  Google Scholar 

  21. 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 219(3):273–285

    Article  Google Scholar 

  22. Karakoyun M, Baykan N, Hacibeyoglu M (2017) Multi-level thresholding for image segmentation with swarm optimization algorithms. Int Res J Electron Comput Eng 3(3):1–6

    Article  Google Scholar 

  23. Sayed G, Solyman M, Hassanien A (2018) A novel chaotic optimal foraging algorithm for unconstrained and constrained problems and its application in white blood cell segmentation. Neural Comput Appl 31:7633–7664

  24. Wang H, Cheng X, Chen G (2021) A hybrid adaptive quantum behaved particle swarm optimization algorithm based multilevel thresholding for image segmentation. In: 2021 IEEE international conference on information communication and software engineering (ICICSE), pp 97–102

  25. Anitha J, Immanuel S, Akila S (2021) An efficient multilevel color image thresholding based on modified whale optimization algorithm. Expert Syst Appl 178:115003

    Article  Google Scholar 

  26. Abd Elaziz M, Nabil N, Moghdani R, Ewees A, Cuevas E, Lu S (2021) Multilevel thresholding image segmentation based on improved volleyball premier league algorithm using whale optimization algorithm. Multimed Tools Appl 80:12435–12468

    Article  Google Scholar 

  27. Farshi R, Taymaz, Ardabili A (2021) A hybrid firefly and particle swarm optimization algorithm applied to multilevel image thresholding. Multimed Syst 27:1–20

    Google Scholar 

  28. Anitha J, Pandian S. Immanuel Alex, Agnes S. Akila (2021) An efficient multilevel color image thresholding based on modified whale optimization algorithm. Expert Syst Appl 178:115003

    Article  Google Scholar 

  29. Wang B, Gao X, Tao D, Li X (2010) A unified tensor level set for image segmentation. IEEE Trans Syst Man Cybern Part B (Cybern) 40(3):857–867

    Article  Google Scholar 

  30. Wang G, Li W, Zuluaga M, Pratt R, Patel P, Aertsen M, Doel T, David A, Deprest J, Ourselin S, Vercauteren T (2018) Interactive medical image segmentation using deep learning with image-specific fine tuning. IEEE Trans Med Imaging 37(7):1562–1573

    Article  Google Scholar 

  31. Zhang J, Li Z, Jing P, Liu Y, Su Y (2017) Tensor-driven low-rank discriminant analysis for image set classification. Multimed Tools Appl 78(4):4001–4020

  32. Liang Y, Ouyang K, Jing L, Ruan S, Liu Y, Zhang J, Rosenblum D, Zheng Y (2019) Urbanfm: inferring fine-grained urban flows. In; Proceedings of the 25th ACM SIGKDD international conference on knowledge discovery and data mining, New York, NY, USA. Association for Computing Machinery, pp 3132–3142

  33. Ouyang K, Liang Y, Liu Y, Tong Z, Ruan S, Rosenblum D, Zheng Y (2020) Fine-grained urban flow inference. IEEE Trans Knowl Data Eng 1–21. https://doi.org/10.1109/TKDE.2020.3017104

  34. Steen W (1998) Methodological problems in evolutionary biology. xi. optimal foraging theory revisited. Acta Biotheor 46:321–336

    Article  Google Scholar 

  35. Pyke GH, Pulliam HR, Charnov EL (1977) optimal foraging: a selective review of theory and tests. Q Rev Biol 52(2):37–154

    Article  Google Scholar 

  36. Benioff P (1980) The computer as a physical system: a microscopic quantum mechanical Hamiltonian model of computers as represented by turing machines. J Stat Phys 22(5):563–591

    Article  MathSciNet  MATH  Google Scholar 

  37. Shor P (1997) Polynomial-time algorithms for prime factorization and discrete logarithms on a quantum computer. SIAM J Sci Stat Comput 26:1484–1509

    MathSciNet  MATH  Google Scholar 

  38. Sun J, Feng B, Xu W (2004) Particle swarm optimization with particles having quantum behavior. In: Congress on evolutionary computation, vol 1, pp 325–331

  39. Abbas N, Aftan H (2014) Quantum artificial bee colony algorithm for numerical function optimization. Int J Comput Appl 93(9):28–30

    Google Scholar 

  40. Sayed G, Hassanien A, Azar A (2017) Feature selection via a novel chaotic crow search algorithm. Neural Comput Appl 31:171–188

  41. Digalakis J, Margaritis K (2001) On benchmarking functions for genetic algorithms. Int J Comput Math 77:481–506

    Article  MathSciNet  MATH  Google Scholar 

  42. Yang X-S (2010) Test problems in optimization. Wiley, London

    Google Scholar 

  43. Suganthan PN, Hansen N, Liang JJ, Deb K, Chen Y-P, Auger A, Tiwari S (2005) Problem definitions and evaluation criteria for the cec 2005 special session on real-parameter optimization. Technical report, Nanyang Technological University, Singapore, May 2005 and KanGAL Report 2005005, IIT Kanpur, India

  44. Scarpiniti M, Wanqing S, Chen X, Cattani C, Zio E (2020) Multifractional Brownian motion and quantum-behaved partial swarm optimization for bearing degradation forecasting. Complexity 2020:1–9

  45. Meng X, Liu Y, Gao X, Zhang H (2014) A new bio-inspired algorithm: chicken swarm optimization. In: Advances in swarm intelligence: 5th international conference. ICSI, Cham, pp 86–94

  46. Kennedy J, Eberhart R (1995) Particle swarm optimization. In: IEEE international conference on neural networks, Perth, WA, pp 1942–1948

  47. Mirjalili S, Gandomi AH, Mirjalili SZ, Saremi S, Faris H, Mirjalili SM (2017) Salp swarm algorithm: a bio-inspired optimizer for engineering design problems. Adv Eng Softw 114:163–191

    Article  Google Scholar 

  48. Karaboga D, Basturk B (2007) Artificial bee colony (abc) optimization algorithm for solving constrained optimization problems. In: 12th international fuzzy systems association world congress, vol 4529. Mexico, pp 789–798

  49. Yang X (2010) A new metaheuristic bat-inspired algorithm. Springer, Berlin, pp 65–74

    MATH  Google Scholar 

  50. Naruei I (2021) Coot optimization algorithm. https://www.mathworks.com/matlabcentral/fileexchange/89102-coot-optimization-algorithm. Retrieved 15 May

  51. Chou J, Truong D (2021) A novel metaheuristic optimizer inspired by behavior of jellyfish in ocean. Appl Math Comput 389:125535

    MathSciNet  MATH  Google Scholar 

  52. Dhiman G, Garg M, Nagar A, Kumar V, Dehghani M (2020) A novel algorithm for global optimization: rat swarm optimizer. J Ambient IntelDl Humaniz Comput 12:8457–8482

  53. Kaur S, Awasthi L, Sangal A, Dhiman G (2020) Tunicate swarm algorithm: a new bio-inspired based metaheuristic paradigm for global optimization. Eng Appl Artif Intell 90:103541

    Article  Google Scholar 

  54. Faramarzi A, Heidarinejad M, Mirjalili S, Gandomi A (2020) Marine predators algorithm: a nature-inspired metaheuristic. Expert Syst Appl 152:1–28

  55. Liang JJ, Qu BY, Suganthan PN (2013) Problem definitions and evaluation criteria for the cec 2014 special session and competition on single objective real-parameter numerical optimization. Technical report 201311, Computational Intelligence Laboratory. Zhengzhou University, Zhengzhou China and Technical Report, Nanyang Technological University, Singapore

  56. Qu BY, Liang JJ, Suganthan PN, Chen Q (2014) Problem definitions and evaluation criteria for the cec 2015 special session and competition on single objective multi-niche optimization. Technical report201411B, Computational Intelligence Laboratory. Zhengzhou University, Zhengzhou China and Technical Report, Nanyang Technological University, Singapore

  57. Awad NH, Ali MZ, Suganthan PN, Liang JJ, Qu BY (2016) Problem definitions and evaluation criteria for the cec 2017 special session and competition on single objective real-parameter numerical optimization. Technical report, Nanyang Technological University, Singapore and Jordan University of Science and Technology, Jordan and Zhengzhou University, Zhengzhou China

  58. Gaillard F et al (2020) Radiopaedia.org. http://radiopaedia.org/search?q=CT&scope=all. Retrieved 23 Jan

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Authors and Affiliations

  1. School of Computer Science, Canadian International College (CIC), New Cairo, Egypt

    Gehad Ismail Sayed

  2. Faculty of Computers and Artificial Intelligence, Cairo University, Giza, Egypt

    Gehad Ismail Sayed

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Appendix: list of benchmark functions

Appendix: list of benchmark functions

See Tables 17, 18, 19, 20 and 21.

Table 17 Definition of IEEE CEC 2005 benchmark functions
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Table 18 Properties of IEEE CEC 2005 benchmark functions, lb denotes lower bound, ub denotes upper bound, dim denotes dimensions, opt denotes optimum point
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Table 19 IEEE CEC 2014 benchmark functions
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Table 20 IEEE CEC 2015 benchmark functions
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Table 21 IEEE CEC 2017 benchmark functions
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Sayed, G.I. A novel multilevel thresholding algorithm based on quantum computing for abdominal CT liver images. Evol. Intel. 16, 439–483 (2023). https://doi.org/10.1007/s12065-021-00669-9

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