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Bi-level thresholding using PSO, Artificial Bee Colony and MRLDE embedded with Otsu method

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Abstract

Image segmentation is required to be studied in detail some particular features (areas of interest) of a digital image. It forms an important and exigent part of image processing and requires an exhaustive and robust search technique for its implementation. In the present work we have studied the working of MRLDE, a newly proposed variant of differential evolution combined with Otsu method, a well known image segmentation method for bi-level thresholding. The proposed variant, termed as Otsu+MRLDE, is tested on a set of 10 images and the results are compared with Otsu method and some other well known metaheuristics.

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References

  1. Abuhaiba ISI, Hassan MAS (2011) Image encryption using differential evolution approach in frequency domain. Signal Image Process Int J (SIPIJ) 2(1):51–69

    Article  Google Scholar 

  2. Akay B (2012) A study on particle swarm optimization and artificial bee colony algorithms for multilevel thresholding. Appl Soft Comput 13:3066–3091

    Google Scholar 

  3. Akay B, Karaboga D (2011) Wavelet packets optimization using artificial bee colony algorithm. In: Proceedings of IEEE Congress on Evolutionary Computation (CEC), pp 89–94

  4. Ali M, Pant M (2010) Improving the performance of differential evolution algorithm using cauchy mutation. Soft Comput 15:991–1007

    Google Scholar 

  5. Aslantas V, Tunckanat M (2007) Differential evolution algorithm for segmentation of wound images. In: Proceedings of International Symposium on Intelligent Signal Processing, pp 175–179

  6. Bedi P, Bansal R, Sehgal P (2012) Multimodal biometric authentication using PSO based watermarking. Procedia Technol 4:612–618

    Article  Google Scholar 

  7. Benala TR, Jampala SD, Villa SH, Konathala B (2009) A novel approach to image edge enhancement using artificial bee colony optimization algorithm for hybridized smoothening filters. In: Proceedings of World Congress on Nature & Biologically Inspired Computing (NaBIC-09), IEEE, pp 1071–1076

  8. Chander A, Chatterjee A, Siarry P (2011) A new social and momentum component adaptive PSO algorithm for image segmentation. Expert Syst Appl 38(5):4998–5004

    Article  Google Scholar 

  9. Chandrakala D, Sumathi S (2012) Application of artificial bee colony optimization algorithm for image classification using color and texture feature similarity fusion. ISRN Artif Intell. doi:10.5402/2012/426957

  10. Chen HY, Leou JJ (2012) Saliency-directed color image interpolation using artificial neural network and particle swarm optimization. J Vis Commun Image Represent 23(2):343–358

    Article  Google Scholar 

  11. Chidambaram C, Lopes HS (2010) An improved artificial bee colony algorithm for the object recognition problem in complex digital images using template matching. Int J Nat Comput Res 1(2):54–70

    Article  Google Scholar 

  12. Cuevas E, Sencin-Echauri F, Zaldivar D, Prez-Cisneros M (2012) Multi-circle detection on images using artificial bee colony (ABC) optimization. Soft Comput 16:281–296

    Google Scholar 

  13. Das T, Dulger LC (2009) Signature verification (SV) toolbox: application of PSO-NN. Eng Appl Artif Intell 22(4–5):688–694

    Article  Google Scholar 

  14. Demsar J (2006) Statistical comparisons of classifiers over multiple data sets. J Mach Learn Res 7:1–30

    Google Scholar 

  15. Dorronsoro B, Bouvry P (2011) Improving classical and decentralized differential evolution with new mutation operator and population topologies. IEEE Trans Evol Comput 15(1):67–98

    Article  Google Scholar 

  16. Epitropakis MG, Tasoulis DK, Pavlidis NG, Plagianakos VP, Vrahatis MN (2011) Enhancing differential evolution utilizing proximity-based mutation operators. IEEE Trans Evol Comput 15(1):99–119

    Article  Google Scholar 

  17. Falco ID, Cioppa AD, Maisto D, Tarantino E (2008) Differential evolution as a viable tool for satellite image registration. Appl Soft Comput 8:1453–1462

    Article  Google Scholar 

  18. Fan H, Lampinen J (2003) A trigonometric mutation operation to differentia evolution. J Glob Optim 27:105–129

    Article  MathSciNet  MATH  Google Scholar 

  19. Gong W, Fialho A, Cai Z, Li H (2011) Adaptive strategy selection in differential evolution for numerical optimization: an empirical study. In: Information Sciences, vol. 181. Elsevier, Amsterdam, pp 5364–5386

  20. Helen R, Kamaraj N, Selvi K, Raja Raman V (2011) Segmentation of pulmonary parenchyma in CT lung images based on 2D Otsu optimized by PSO. In: Proceedings of ICETECT 2011, pp 536–541

  21. 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 

  22. Horng MH, Jiang TW (2010) Multilevel image thresholding selection using the artificial bee colony algorithm. In: Wang F, Deng H, Gao Y, Lei J (eds) Artificial intelligence and computational intelligence. Lecture Notes in Computer Science, vol 6320. Springer, Berlin, pp 318–325

  23. Kaelo P, Ali MM (2006) A numerical study of some modified differential evolution algorithms. Eur J Oper Res 169:1176–1184

    Article  MathSciNet  MATH  Google Scholar 

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

  25. Kennedy I, Eberhart RC (1995) Particle swarm optimization. In: Proceedings of IEEE International Conference on Neural Networks, pp 1942–1948

  26. Kumar P, Pant M (2012) Enhanced mutation strategy for differential evolution. In: Proceedings of IEEE Congress on Evolutionary Computation (CEC 12), pp 1–6

  27. Kumar S, Pant M, Ray AK (2011) Differential evolution embedded Otsu’s method for optimized image thresholding. In: Proceedings of World Congress in Information and Communication Technology (WICT-11), pp 325–329

  28. Kumar S, Sharma TK, Pant M, Ray AK (2012) Adaptive artificial bee colony for segmentation of CT lung images. Int J Comp App iRAFIT 5:1–5

    Google Scholar 

  29. Lai JCY, Leung FHF, Ling SH (2009) A new differential evolution with wavelet theory based mutation operation. In: Proceedings of IEEE Congress on Evolutionary Computation (CEC 09), pp 1116–1122

  30. Lee CY, Leou JJ, Hsiao HH (2012) Saliency-directed color image segmentation using modified particle swarm optimization. Signal Process 92(1):1–18

    Article  Google Scholar 

  31. Liu F, Duan H, Deng Y (2012) A chaotic quantum-behaved particle swarm optimization based on lateral inhibition for image matching. Optik Int J Light Electron Optics 123:1955–1960

    Google Scholar 

  32. Ma M, Liang J, Guo M, Fan Y, Yin Y (2011) Sar image segmentation based on artificial bee colony algorithm. Appl Soft Comput 11(8):5205–5214

    Article  Google Scholar 

  33. 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

    Article  Google Scholar 

  34. Mohamed S, Roomi M, Bhargavi R, Bhumesh S (2012) Visual model based single image dehazing using artificial bee colony optimization. Int J Inf Sci Tech 2(3):77–88

    Google Scholar 

  35. Muruganandham A, Banu RSD (2010) Adaptive fractal image compression using PSO. Procedia Comput Sci 2:338–344

    Article  Google Scholar 

  36. Nebti S, Boukerram A (2010) Handwritten digits recognition based on swarm optimization methods. In: Zavoral F, Yaghob J, Pichappan P, El-Qawasmeh E (eds) Networked digital technologies, pt 1, vol 87. Communication Computer Information Science. Springer, Berlin, pp 45–54

  37. Neri F, Tirronen V (2009) Scale factor local search in differential evolution. Memet Comput 1:153–171

    Google Scholar 

  38. Neri F, Tirronen V (2010) Recent advances in differential evolution: a survey and experimental analysis. Artif Intell Rev 33(1–2):61–106

    Article  Google Scholar 

  39. Niraimathi P, Sudhakar MS, Bagan KB (2012) Efficient reordering algorithm for color palette image using adaptive particle swarm technique. Appl Soft Comput 12(8):2199–2207

    Article  Google Scholar 

  40. Omran MG, Engelbrecht AP, Salman A (2002) Image classification using particle swarm optimization. In: Proceedings of the 4th Asia-Pacific Conference on Simulated Evolution and Learning, pp 370–374

  41. Omran MG, Engelbrecht AP, Salman A (2004) Particle swarm optimization for pattern recognition and image processing. In: Swarm Intelligence in Data Mining, pp 125–151

  42. Omran MG, Engelbrecht AP, Salman A (2005) Dynamic clustering using particle swarm optimization with application in unsupervised image classification. In: Proceedings of Fifth World Enformatika Conference (ICCI 2005), Prague, Czech Republic, pp 199–204

  43. Omran MG, Engelbrecht AP, Salman A (2006) Dynamic clustering using particle swarm optimization with application in image segmentation. Pattern Anal Appl 8(4):332–344

    Article  MathSciNet  Google Scholar 

  44. Omran MG, Engelbrecht AP, Salman A (2005) Acolor image quantization algorithm based on particle swarm optimization. Informatica (Ljubljana) 29(3):261

    MATH  Google Scholar 

  45. Omran MGH, Engelbrecht AP, Salman A (2005) Differential evolution methods for unsupervised image classification. Proc IEEE Congr Evol Comput 2:966–973

    Google Scholar 

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

    Article  Google Scholar 

  47. Pant M, Ali M, Abraham A (2009) Mixed mutation strategy embedded differential evolution. In: Proceeding of IEEE Congress on Evolutionary Computation (CEC-09), pp 1240–1246

  48. Pant M, Thangaraj R, Abraham A, Grosan C (2005) Differential evolution with laplace mutation operator. In: Proceedings of IEEE Congress on Evolutionary Computation (CEC-05), Norway, pp 2841–2849

  49. Pavan KK, Srinivas VS, Srikrishna A, Reddy BE (2012) Automatic tissue segmentation in medical image using differential evolution. J Appl Sci 12(6):587–592

    Article  Google Scholar 

  50. Qin AK, Huang VL, Suganthan PN (2009) Differential evolution algorithm with strategy adaptation for global numerical optimization. IEEE Trans Evol Comput 13(2):398–417

    Article  Google Scholar 

  51. Rahnamayan S, Tizhoosh HR, Salama MMA (2006) Image thresholding using differential evolution. In: International Conference on Image Processing, Computer Vision, and Pattern Recognition (IPCV-2006), Las Vegas, USA, pp 244–249

  52. Rahnamayan S, Tizhoosh HR (2008) Image thresholding using micro opposition based differential evolution. In: Proceedings of IEEE CEC 2008, pp 1409–1416

  53. Sathya PD, Kayalvizhi R (2011) Optimal segmentation of brain MRI based on adaptive bacterial foraging algorithm. Neurocomputing 74(14–15):2299–2313

    Article  Google Scholar 

  54. Storn R, Price K (1997) Differential evolution—a simple and efficient heuristic for global optimization over continuous spaces. J Glob Optim 11(4):341–359

    Article  MathSciNet  MATH  Google Scholar 

  55. Sudhakar G, Babu PV, Satapathy SC, Pradhan G (2010) Effective image clustering with differential evolution technique. Int J Comput Commun Tech 2(1):11–19

    Google Scholar 

  56. Tsai HH, Jhuang YJ (2012) An SVD-based image watermarking in wavelet domain using SVR and PSO. Appl Soft Comput 12(8):2442–2453

    Article  Google Scholar 

  57. Wachowiak MP, Smolikova R, Elmaghraby AS (2004) An approach to multimodal biomedical image registration utilizing particle swarm optimization. IEEE Trans Evol Comput 8(3):289–301

    Article  MathSciNet  Google Scholar 

  58. Wang S (2011) Artificial bee colony used for rigid image registration. Int J Res Rev Soft Intell Comput 1(2):33–36

    Article  Google Scholar 

  59. Xu C, Duan H (2010) Artificial bee colony (abc) optimized edge potential function (epf) approach to target recognition for low-altitude aircraft. Pattern Recognit Lett 31(13, SI):1759–1772

    Article  Google Scholar 

  60. Ye Z, Zeng M, Hu Z, Chen H (2011) Image enhancement based on artificial bee colony algorithm and fuzzy set. doi:10.1115/1.859759.paper30

  61. Yin PY (2007) Multilevel minimum cross entropy threshold selection based on particle swarm optimization. Appl Math Comput 184(2):503–513

    Article  MathSciNet  MATH  Google Scholar 

  62. Zhang J, Sanderson A (2009) JADE: adaptive differential evolution with optional external archive. IEEE Trans Evol Comput 13(2):945–958

    Article  Google Scholar 

  63. Zhang Y, Huang D, Ji M, Xie F (2011) Image segmentation using PSO and PCM with Mahalanobis distance. Expert Syst Appl 38(7):9036–9040

    Article  Google Scholar 

  64. Zhang Y, Wu L (2011) Optimal multi-level thresholding based on maximum tsallis entropy via an artificial bee colony approach. Entropy 13(4):841–859

    Article  MATH  Google Scholar 

  65. Zhang Y, Wu L, Wang S (2011) Magnetic resonance brain image classification by an improved artificial bee colony algorithm. Prog Electromagn Res-PIER 116:65–79

    Google Scholar 

  66. Zhiwei Y, Zhaobao Z, Xin Y, Xiaogang N (2006) Automatic threshold selection based on ant colony optimization algorithm. In: Proceedings of the International Conference on Neural Networks and Brain, Beijing, pp 728–732

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Authors and Affiliations

  1. Department of Applied Science and Engineering, IIT Roorkee, Roorkee, India

    Sushil Kumar, Pravesh Kumar, Tarun Kumar Sharma & Millie Pant

Authors
  1. Sushil Kumar
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  2. Pravesh Kumar
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  3. Tarun Kumar Sharma
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Correspondence to Sushil Kumar.

Appendix: Pseudo code of PSO, and ABC

Appendix: Pseudo code of PSO, and ABC

1.1 Particle Swarm Optimization

Particle Swarm Optimization (PSO) proposed by Kennedy and Eberhart [25], is a stochastic, population set based nature inspired optimization algorithm. In a PSO system, a swarm of individuals (called particles) fly through the search space. Each particle represents a candidate solution of the optimization problem. The position of a particle is influenced by the best position visited by itself and best position of a particle in its neighborhood. Suppose the position and velocity of the \(ith\) particle in \(D\)-dimensional space at any generation \(G\), are represented as \(X_{i,G} =\{x_{i,1, G} , x_{i, 2, G} ,\ldots ,x_{i, D, G} \}\) and \(V_{i,G} =\{v_{i,1, G} , v_{i, 2, G} ,\ldots ,v_{i, D, G} \}\) respectively. The pseudo code of Otsu embedded PSO for image segmentation is given below;

figure b

1.2 Artificial Bee Colony

The Artificial Bee Colony algorithm, developed by Karaboga [24] mimics the foraging behaviour of honey bees. A pseudo code for Otsu embedded ABC method for image segmentation is given below;

figure c

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Kumar, S., Kumar, P., Sharma, T.K. et al. Bi-level thresholding using PSO, Artificial Bee Colony and MRLDE embedded with Otsu method. Memetic Comp. 5, 323–334 (2013). https://doi.org/10.1007/s12293-013-0123-5

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