Skip to main content
SpringerLink
Log in

A novel opposition based improved firefly algorithm for multilevel image segmentation

  • Published:
Multimedia Tools and Applications Aims and scope Submit manuscript

Abstract

The data explosion caused by the Internet and its applications has given researchers immense scope for data analysis. A large amount of data is available in form of images. Image processing is required for better understandability of an image. Various image processing steps are available for improving the image in different application areas. Various applications like medical imaging, face recognition, biometric security, and traffic surveillance, etc. depend only on image and its analysis. This analysis in several applications is highly dependent on the outcome of image segmentation. This paper focuses on good segmentation through multi-level thresholding. In this research, the algorithm includes two modules related to Entropy and variance. The first module is concerned with the modified firefly algorithm (FA) with Kapur’s, Tsallis, and Fuzzy Entropy. FA is used to optimize fuzzy parameters for obtaining optimal thresholds. The second module is derived from the principle of variance between two classes known as between variance or inter-cluster variance. The opposition-based the learning method is used for initializing the population of candidate solutions and levying flight and local search is implemented with FA. The various experiments have been performed on Berkeley and benchmark images with distinct threshold (i.e. 2, 3, 4, 5) values. The proposed algorithm has been estimated and compared with known metaheuristic optimization methods like particle swarm optimization (PSO) and electromagnetism optimization (EMO). The results have been assessed quantitatively and qualitatively by using parameters like Peak signal-to-noise ratio (PSNR), structured similarity index metric (SSIM), objective function values, and convergence curve. The algorithm proposed observed better experiment results than PSO, EMO in terms of persistency and quality.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11

Similar content being viewed by others

References

  1. Abdullah-Al-Wadud M, Chae O (2008) Skin segmentation using color distance map and water-flow property. In 2008 the fourth international conference on information assurance and security (pp. 83-88). IEEE

  2. Anitha P, Bindhiya S, Abinaya A, Satapathy SC, Dey N, Rajinikanth V (2017) RGB image multi-thresholding based on Kapur's entropy—a study with heuristic algorithms. In 2017 second international conference on electrical, computer and communication technologies (ICECCT) (pp. 1-6). IEEE

  3. Ansar W, Bhattacharya T (2016) A new gray image segmentation algorithm using cat swarm optimization. In 2016 international conference on communication and signal processing (ICCSP) (pp. 1004-1008). IEEE

  4. Bagri N, Johari PK (2015) A comparative study on feature extraction using texture and shape for content based image retrieval‖. Int J Advanced Sci Technol 80:41–52

    Article  Google Scholar 

  5. Bejinariu SI, Costin H, Rotaru F, Luca R, & Niţă CD (2015) Automatic multi-threshold image segmentation using metaheuristic algorithms. In 2015 international symposium on signals, circuits and systems (ISSCS) (pp. 1-4). IEEE

  6. Bejinariu SI, Luca R, Costin H (2018) Metaheuristic algorithms based multi-objective optimization for image segmentation. In 2018 international conference and exposition on electrical and power engineering (EPE) (pp. 0438-0443). IEEE.

  7. Berkley Image segmentation data set. https://www2.eecs.berkeley.edu/Research/Projects/CS/vision/bsds (n.d.)

  8. 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

    Article  Google Scholar 

  9. Bhandari AK, Kumar IV, Srinivas K (2019) Cuttlefish algorithm based multilevel 3D Otsu function for color image segmentation. IEEE Trans Instrum Meas 69:1871–1880

    Article  Google Scholar 

  10. Bhargava A, Bansal A (2021) Novel Coronavirus (COVID-19) Diagnosis using computer vision and artificial intelligence techniques: A Review. Multimedia Tools Appl 385:8

    Google Scholar 

  11. Bhargava A, Bansal A (2021) Fruits and vegetables quality evaluation using computer vision: a review. J King Saud Univ Comput Inform Sci 13(3):243–257

    Google Scholar 

  12. Bozkurt ÖÖ, Biricik G, Tayşi ZC (2017) Artificial neural network and SARIMA based models for power load forecasting in Turkish electricity market. PLoS One 12(4):e0175915

    Article  Google Scholar 

  13. Canayaz M, Hanbay K (2016) Neutrosophic set based image segmentation approach using cricket algorithm. In 2016 international symposium on INnovations in intelligent SysTems and applications (INISTA) (pp. 1-5). IEEE

  14. Chao Y, Dai M, Chen K, Chen P, Zhang Z (2016) Fuzzy entropy based multilevel image thresholding using modified gravitational search algorithm. In 2016 IEEE international conference on industrial technology (ICIT) (pp. 752-757)

  15. Chaudhry A, Dokania PK, Torr PHS (2017) Discovering class-specific pixels for weakly-supervised semantic segmentation

  16. 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:–12

  17. Chinta S, Tripathy BK, & Rajulu KG (2017) Kernelized intuitionistic fuzzy C-means algorithms fused with firefly algorithm for image segmentation. In 2017 international conference on microelectronic devices, circuits and systems (ICMDCS) (pp. 1-6). IEEE.

  18. Cufoglu A, Lohi M, Everiss C (2017) Feature weighted clustering for user profiling. Int J Model Simul Sci Comput 08(4):30–315

    Article  Google Scholar 

  19. De Albuquerque MP, Esquef IA, Mello AG (2004) Image thresholding using Tsallis entropy. Pattern Recogn Lett 25(9):1059–1065

    Article  Google Scholar 

  20. Dong W, Li H, Wei X et al (2017) An efficient iterative thresholding method for image segmentation. J Comput Phys 350

  21. Ghamisi P, Couceiro MS, Martins FM, Benediktsson JA (2013) Multilevel image segmentation based on fractional-order Darwinian particle swarm optimization. IEEE Trans Geosci Remote Sens 52(5):2382–2394

    Article  Google Scholar 

  22. Gonzalez RC, Woods RE (2002) Digital Image Processing, second ed., Prentice Hall Upper Saddle River NJ

  23. Hamdaoui F, Sakly A, Mtibaa A (2015). Real-time synchronous hardware architecture for MRI images segmentation based on PSO. In 2015 4th international conference on systems and control (ICSC) (pp. 498-503). IEEE

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

  25. Huang KW, Chen JL, Yang CS, Tsai CW (2015) A memetic gravitation search algorithm for solving clustering problems. In 2015 IEEE congress on evolutionary computation (CEC) (pp. 751-757). IEEE.

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

    Article  Google Scholar 

  27. 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 

  28. Kaur A (2016) An automatic brain tumor extraction system using different segmentation methods. In 2016 second international conference on Computational Intelligence & Communication Technology (CICT) (pp. 187-191). IEEE

  29. Khomri B, Christodoulidis A, Djerou L, Babahenini MC, Cheriet F (2018) Retinal blood vessel segmentation using the elite-guided multi-objective artificial bee colony algorithm. IET Image Process 12(12):2163–2171

    Article  Google Scholar 

  30. Kumar M, Dubey K and Pandey R (2021) "Evolution of Emerging Computing paradigm Cloud to Fog: Applications, Limitations and Research Challenges," 2021 11th International Conference on Cloud Computing, Data Science & Engineering (Confluence), pp. 257–261, https://doi.org/10.1109/Confluence51648.2021.9377050.

  31. Kumar V, Chhabra JK, Kumar D (2014) Automatic cluster evolution using gravitational search algorithm and its application on image segmentation. Eng Appl Artif Intell 29:93–103

    Article  Google Scholar 

  32. Kurban T, Civicioglu P, Kurban R, Besdok E (2014) Comparison of evolutionary and swarm-based computational techniques for multilevel color image thresholding. Appl Soft Comput 23:128–143

    Article  Google Scholar 

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

    Article  Google Scholar 

  34. Liu S, Wang Y (2021) Journal of Physics: Conference Series, Volume 1865, 2021 International Conference on Advances in Optics and Computational Sciences (ICAOCS) 2021 21–23 January. J Phys: Conf Ser 1865:042098

    Google Scholar 

  35. Mohit K, Sharma SC (2018) Deadline constrained based dynamic load balancing algorithm with elasticity in cloud environment. Comput Electrical Eng 69:395–411, ISSN 0045-7906. https://doi.org/10.1016/j.compeleceng.2017.11.018

    Article  Google Scholar 

  36. Mohit K, Sharma SC, Goel A, Singh SP (2019) A comprehensive survey for scheduling techniques in cloud computing. J Network Comput Appl 143:1–33, ISSN 1084-8045. https://doi.org/10.1016/j.jnca.2019.06.006

    Article  Google Scholar 

  37. Mohit K, Sharma SC (2018) PSO-COGENT: Cost and energy efficient scheduling in cloud environment with deadline constraint. Sustain Comput: Inform Syst 19:147–164, ISSN 2210-5379. https://doi.org/10.1016/j.suscom.2018.06.002

    Article  Google Scholar 

  38. Mousavirad SJ, Ebrahimpour-Komleh H (2017) Multilevel image thresholding using entropy of histogram and recently developed population-based metaheuristic algorithms. Evol Intel 10(1–2):45–75

    Article  Google Scholar 

  39. Mozaffari MH, Lee WS (2017) Convergent heterogeneous particle swarm optimisation algorithm for multilevel image thresholding segmentation. IET Image Process 11(8):605–619

    Article  Google Scholar 

  40. Muangkote N, Sunat K, & Chiewchanwattana S (2016) Multilevel thresholding for satellite image segmentation with moth-flame based optimization. In 2016 13th international joint conference on computer science and software engineering (JCSSE) (pp. 1-6). IEEE

  41. Ng HF (2006) Automatic thresholding for defect detection. Pattern Recogn Lett 27(14):1644–1649

    Article  Google Scholar 

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

    Article  Google Scholar 

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

    Article  Google Scholar 

  44. Panda R, Agrawal S, Bhuyan S (2013) Edge magnitude based multilevel thresholding using cuckoo search technique. Expert Syst Appl 40(18):7617–7628

    Article  Google Scholar 

  45. Preetha MMSJ, Padmasuresh L, & Bosco MJ (2016) Firefly based region growing and region merging for image segmentation. In 2016 international conference on emerging technological trends (ICETT) (pp. 1-9). IEEE.

  46. Rajinikanth V, Dey N, Kavallieratou E, Lin H (2020) Firefly algorithm-based Kapur’s thresholding and Hough transform to extract leukocyte section from hematological images. In: Dey N (ed) Applications of firefly algorithm and its variants. Springer Tracts in Nature-Inspired Computing. Springer, Singapore. https://doi.org/10.1007/978-981-15-0306-1_10

    Chapter  Google Scholar 

  47. Rapaka S, Kumar PR (2018) Efficient approach for non-ideal iris segmentation using improved particle swarm optimisation-based multilevel thresholding and geodesic active contours. IET Image Process 12(10):1721–1729

    Article  Google Scholar 

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

    Article  Google Scholar 

  49. Sharma A, Sehgal S. (2016). Image segmentation using firefly algorithm. In 2016 international conference on information technology (InCITe)-the next generation IT summit on the theme-internet of things: connect your worlds (pp. 99-102). IEEE.

  50. Singh G, Ansari MA (2016) Efficient detection of brain tumor from MRIs using K-means segmentation and normalized histogram. In 2016 1st India international conference on information processing (IICIP) (pp. 1-6). IEEE.

  51. Singh R, Agarwal P, Kashyap M, Bhattacharya M (2016) Kapur's and Otsu's based optimal multilevel image thresholding using social spider and firefly algorithm. In 2016 international conference on communication and signal processing (ICCSP) (pp. 2220-2224). IEEE

  52. Somwanshi D, Kumar A, Sharma P, Joshi D (2016) An efficient brain tumor detection from MRI images using entropy measures. In 2016 international conference on recent advances and Innovations in engineering (ICRAIE) (pp. 1-5). IEEE

  53. Sridevi M (2017) Image segmentation based on multilevel thresholding using firefly algorithm. In 2017 international conference on inventive computing and informatics (ICICI) (pp. 750-753). IEEE

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

    Article  Google Scholar 

  55. Tsallis C (1988) Possible generalization of Boltzmann-Gibbs statistics. J Stat Phys 52(1–2):479–487

    Article  MathSciNet  Google Scholar 

  56. Turajlić E (2018) Application of firefly and bat algorithms to multilevel thresholding of X-ray images. In 2018 41st international convention on information and communication technology, electronics and microelectronics (MIPRO) (pp. 1104-1109). IEEE

  57. Yang XS. (2009). Firefly algorithms for multimodal optimization. In international symposium on stochastic algorithms (pp. 169-178). Springer, Berlin, Heidelberg.

  58. Yang ZH, Pu ZB, Qi ZQ (2003) Relative entropy multilevel thresholding method based on genetic optimization. In international conference on neural networks and signal processing, 2003. Proceedings of the 2003 (Vol. 1, pp. 583-586). IEEE

  59. Yu C, Jin B, Lu Y, Chen X, Yi Z, Zhang K, Wang S (2013) Multi-threshold image segmentation based on firefly algorithm. In 2013 ninth international conference on intelligent information hiding and multimedia signal processing (pp. 415-419). IEEE

  60. Zhao D, Liu L, Yu F et al (2020) Chaotic random spare ant colony optimization for multi-threshold image segmentation of 2D Kapur entropy. Knowledge-Based Syst. https://doi.org/10.1016/j.knosys.2020.106510

  61. Zhao F, Chen Y, Liu H, Fan J (2019) Alternate PSO-based adaptive interval Type-2 intuitionistic fuzzy C-means clustering algorithm for color image segmentation. IEEE Access 7:64028–64039

    Article  Google Scholar 

  62. Zhou C, Tian L, Zhao H, Zhao K (2015) A method of two-dimensional Otsu image threshold segmentation based on improved firefly algorithm. In 2015 IEEE international conference on Cyber Technology in Automation, control, and intelligent systems (CYBER) (pp. 1420-1424). IEEE

Download references

Funding

This research is not having any specific grant from funding agencies in the public, commercial, or not for profit sectors.

Author information

Authors and Affiliations

  1. MIT SOE, MIT ADT University, Pune, India

    Abhay Sharma & Rekha Chaturvedi

  2. GLA University, Mathura, India

    Anuja Bhargava

Authors
  1. Abhay Sharma
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Rekha Chaturvedi
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Anuja Bhargava
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Abhay Sharma.

Ethics declarations

Conflict of interest

Authors do not have any conflicts.

Additional information

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Sharma, A., Chaturvedi, R. & Bhargava, A. A novel opposition based improved firefly algorithm for multilevel image segmentation. Multimed Tools Appl 81, 15521–15544 (2022). https://doi.org/10.1007/s11042-022-12303-6

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11042-022-12303-6

Keywords

Search

Navigation