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A multilevel thresholding algorithm using LebTLBO for image segmentation

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Abstract

Segmentation is considered as one of the most significant tasks in image processing. It consists of separating the pixels into different segments based on their intensity level according to threshold values. Selecting the optimal threshold value is the key to best quality segmentation. Multilevel thresholding (MT) is an essential approach for image segmentation, and it has become very popular during the past few years, but while increasing the level of thresholds, computational complexity also increases exponentially. In order to overcome this drawback, several metaheuristics-based algorithms have been used for determining the optimal MT levels. Learning enthusiasm-based teaching–learning-based optimization (LebTLBO) is a recently developed efficient, simple-to-implement and computationally inexpensive algorithm. It simulates the behaviors of the teaching and learning process in a classroom and gives the probability of getting the amount of information by the learner (student) from the educator. In this paper, LebTLBO is applied on ten standard test images having a diverse histogram, which are taken from Berkeley Segmentation Dataset 500 (BSDS500) (Martin et al. in a database of human segmented natural images and its application to evaluate segmentation algorithms and measure ecological statistics, 2001) benchmark image set for segmentation. The search capability of the algorithm is combined with Otsu and Kapur’s entropy MT objective functions for image segmentation. The proposed approach is compared with the existing state-of-the-art optimization algorithms such as MTEMO, GA, PSO and BF for both Otsu and Kapur’s entropy methods. Qualitative experimental outcomes demonstrate that LebTLBO is highly efficient in terms of performance metrics such as PSNR, mean, threshold values, number of iterations taken to converge and image segmentation quality.

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References

  1. Kamel M, Zhao A (1993) Extraction of binary character/graphics images from grayscale document images. CVGIP: Graph Models Image Process 55(3):203–217

    Google Scholar 

  2. Marinoni A, Plaza A, Gamba P (2017) A novel preunmixing framework for efficient detection of linear mixtures in hyperspectral images. IEEE Trans Geosci Remote Sens 55(8):4325–4333

    Article  Google Scholar 

  3. Abak AT, Baris U, Sankur B (1997) The performance evaluation of thresholding algorithms for optical character recognition. In: Proceedings of the fourth international conference on document analysis and recognition, vol 2, pp 10–13

  4. Shubham S, Bhandari AK (2019) A generalized Masi entropy based efficient multilevel thresholding method for color image segmentation. Multimed Tools Appl 78:1–42

    Article  Google Scholar 

  5. Bohat VK, Arya KV (2019) A new heuristic for multilevel thresholding of images. Expert Syst Appl 117:176–203

    Article  Google Scholar 

  6. Verma OP, Parihar AS (2017) An optimal fuzzy system for edge detection in color images using bacterial foraging algorithm. IEEE Trans Fuzzy Syst 25(1):114–127

    Article  Google Scholar 

  7. Li L, Sun L, Guo J, Han C, Zhou J, Li S (2017) A quick artificial bee colony algorithm for image thresholding. Information 8(1):16

    Article  Google Scholar 

  8. Abdel-Khalek S, Ben Ishak A, Omer OA, Obada ASF (2017) A two-dimensional image segmentation method based on genetic algorithm and entropy. Optik (Stuttg) 131:414–422

    Article  Google Scholar 

  9. Ye Z, Yang J, Wang M, Zong X, Yan L, Liu W (2018) 2D Tsallis entropy for image segmentation based on modified chaotic bat algorithm. Entropy 20(4):1–28

    Article  Google Scholar 

  10. Bhandari AK, Singh VK, Kumar A, Singh GK (2014) Cuckoo search algorithm and wind driven optimization based study of satellite image segmentation for multilevel thresholding using Kapur’s entropy. Expert Syst Appl 41(7):3538–3560

    Article  Google Scholar 

  11. Abd M, Aziz E, Ewees AA, Ella A (2017) Whale optimization algorithm and moth-flame optimization for multilevel thresholding image segmentation. Expert Syst Appl 83:242–256

    Article  Google Scholar 

  12. Jia H, Ma JUN, Song W (2019) Multilevel thresholding segmentation for color image using modified moth-flame optimization. IEEE Access 7:44097–44134

    Article  Google Scholar 

  13. Horng M (2010) A multilevel image thresholding using the honey bee mating optimization. Appl Math Comput 215(9):3302–3310

    MathSciNet  MATH  Google Scholar 

  14. Brest J, Zumer V, Maucec MS (2006) Self-adaptive differential evolution algorithm in constrained real-parameter optimization. In: 2006 IEEE international conference on evolutionary computation. IEEE, pp 215–222

  15. Liang H, Jia H, **ng Z, Ma J, Peng X (2019) Modified grasshopper algorithm-based multilevel thresholding for color image segmentation. IEEE Access 7:11258–11295

    Article  Google Scholar 

  16. He L, Huang S (2017) Modified firefly algorithm based multilevel thresholding for color image segmentation. Neurocomputing 240:152–174

    Article  Google Scholar 

  17. Wang XN, Feng YJ, Feng ZR (2005) Ant colony optimization for image segmentation. In: 2005 international conference on machine learning and cybernetics. vol 9. IEEE, pp. 5355–5360

  18. Hemeida AM, Mansour R, Hussein ME (2018) Multilevel thresholding for image segmentation using an improved electromagnetism optimization algorithm. IJIMAI 5:102–112

    Article  Google Scholar 

  19. Tuba M (2014) Multilevel image thresholding by nature-inspired algorithms: a short review. ICISP 22(3):318–338

    MathSciNet  Google Scholar 

  20. Lei B, Fan J, Fan J (2019) Adaptive Kaniadakis entropy thresholding segmentation algorithm based on particle swarm optimization. Soft Comput 2:1–14

    Google Scholar 

  21. Yamamura Y, Kim H, Yamamoto A (2006) A method for image registration by maximization of mutual information. In: 2006 SICE-ICASE International joint conference. IEEE, pp 1469–1472

  22. Shi J, Ray N, Zhang H (2012) Shape based local thresholding for binarization of document images. Pattern Recognit Lett 33(1):24–32

    Article  Google Scholar 

  23. Elon JD (2007) A non parametric theory for histogram segmentation. IEEE Trans Image Process 16(1):23–261

    Google Scholar 

  24. El-Sayed MA (2011) Study of efficient technique based on 2D Tsallis entropy for image thresholding. Int J Comput Sci Eng 3(9):3125–3138

    Google Scholar 

  25. Zhang H, Zhu Q, Guan XF (2012) Probe into image segmentation based on Sobel operator and maximum entropy algorithm. In: Proceedings—2012 international conference on computer science service system. CSSS 2012, pp 238–241

  26. Sahoo PK, Arora G (2006) Image thresholding using two-dimensional Tsallis–Havrda–Charvát entropy. Pattern Recognit Lett 27(6):520–528

    Article  Google Scholar 

  27. El Munim HEA, Farag AA (2005) A shape-based segmentation approach: an improved technique using level sets. In: Proceedings of the IEEE international conference on computer vision, vol 2, pp 930–935

  28. Feng D, Wenkang S, Liangzhou C, Yong D, Zhenfu Z (2005) Infrared image segmentation with 2-D maximum entropy method based on particle swarm optimization (PSO). Pattern Recognit Lett 26(5):597–603

    Article  Google Scholar 

  29. Zheng S, Cheng MM, Warrell J, Sturgess P, Vineet V, Rother C, Torr PH (2014) Dense semantic image segmentation with objects and attributes. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 3214–3221

  30. Shi Z, Yang Y, Hospedales TM, **ang T (2017) Weakly-supervised image annotation and segmentation with objects and attributes. IEEE Trans Pattern Anal Mach Intell 39(12):2525–2538

    Article  Google Scholar 

  31. Pal C, Chakrabarti A, Ghosh R (2015) A brief survey of recent edge-preserving smoothing algorithms on digital images. ar**v:1503.07297

  32. Smith P, Reid DB, Environment C, Palo L, Alto P, Smith PL (1979) A threshold selection method from gray-level histograms. IEEE Trans Syst Man Cybern 9(1):62–66

    Article  Google Scholar 

  33. Pal NR (1989) Entropic thresholding. Signal Process 16:97–108

    Article  MathSciNet  Google Scholar 

  34. 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 

  35. Saeedi J, Faez K (2012) Infrared and visible image fusion using fuzzy logic and population-based optimization. Appl Soft Comput J 12(3):1041–1054

    Article  Google Scholar 

  36. Valipour M (2016) Optimization of neural networks for precipitation analysis in a humid region to detect drought and wet year alarms. Meteorol Appl 23(1):91–100

    Article  Google Scholar 

  37. Azarbad M, Ebrahimzade A, Izadian V (2011) Segmentation of infrared images and objectives detection using maximum entropy method based on the bee algorithm. Int J Comput Inf Syst Ind Manag Appl 3:26–33

    Google Scholar 

  38. Sreeja P, Hariharan S (2018) An improved feature based image fusion technique for enhancement of liver lesions. Biocybern Biomed Eng 38:611–623

    Article  Google Scholar 

  39. Portes de Albuquerque M, Esquef IA, Gesualdi Mello AR, Portes de Albuquerque M (2004) Image thresholding using Tsallis entropy. Pattern Recognit Lett 25(9):1059–1065

    Article  Google Scholar 

  40. Rao RV, Patel V (2013) Multi-objective optimization of heat exchangers using a modified teaching-learning-based optimization algorithm. Appl Math Model 37(3):1147–1162

    Article  MathSciNet  MATH  Google Scholar 

  41. Singh M, Panigrahi BK, Abhyankar AR (2013) Electrical power and energy systems optimal coordination of directional over-current relays using teaching learning-based optimization (TLBO) algorithm. Int J Electr Power Energy Syst 50:33–41

    Article  Google Scholar 

  42. Chen X, Xu B, Yu K, Du W (2018) Teaching-learning-based optimization with learning enthusiasm mechanism and its application in chemical engineering. J Appl Math. https://doi.org/10.1155/2018/1806947

    Article  MATH  Google Scholar 

  43. Storn R (1997) Differential evolution—a simple and efficient heuristic for global optimization over continuous spaces. J Glob Optim 11:341–359

    Article  MathSciNet  MATH  Google Scholar 

  44. Kaur R, Singh S (2017) An artificial neural network based approach to calculate BER in CDMA for multiuser detection using MEM. In: Proceedings of the 2016 2nd international conference on next generation computing technologies. NGCT 2016, pp 450–455

  45. Martin D, Fowlkes C, Tal D, Malik J (2001) A database of human segmented natural images and its application to evaluating segmentation algorithms and measuring ecological statistics. In: Proceedings eighth IEEE international conference on computer vision. ICCV 2001, vol 2. IEEE, pp 416–423

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Authors and Affiliations

  1. Department of Electronics and Communication Engineering, Chandigarh University, Gharuan, Punjab, India

    Simrandeep Singh & Nitin Mittal

  2. Department of Electronics and Communication Engineering, Chandigarh Engineering College, Landran, Punjab, India

    Harbinder Singh

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Singh, S., Mittal, N. & Singh, H. A multilevel thresholding algorithm using LebTLBO for image segmentation. Neural Comput & Applic 32, 16681–16706 (2020). https://doi.org/10.1007/s00521-020-04989-2

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