Advertisement

Neural Computing and Applications

, Volume 29, Issue 12, pp 1285–1307 | Cite as

Multi-level image thresholding using Otsu and chaotic bat algorithm

  • Suresh Chandra Satapathy
  • N. Sri Madhava Raja
  • V. Rajinikanth
  • Amira S. AshourEmail author
  • Nilanjan Dey
Original Article

Abstract

Multi-level thresholding is a helpful tool for several image segmentation applications. Evaluating the optimal thresholds can be applied using a widely adopted extensive scheme called Otsu’s thresholding. In the current work, bi-level and multi-level threshold procedures are proposed based on their histogram using Otsu’s between-class variance and a novel chaotic bat algorithm (CBA). Maximization of between-class variance function in Otsu technique is used as the objective function to obtain the optimum thresholds for the considered grayscale images. The proposed procedure is applied on a standard test images set of sizes (512 × 512) and (481 × 321). Further, the proposed approach performance is compared with heuristic procedures, such as particle swarm optimization, bacterial foraging optimization, firefly algorithm and bat algorithm. The evaluation assessment between the proposed and existing algorithms is conceded using evaluation metrics, namely root-mean-square error, peak signal to noise ratio, structural similarity index, objective function, and CPU time/iteration number of the optimization-based search. The results established that the proposed CBA provided better outcome for maximum number cases compared to its alternatives. Therefore, it can be applied in complex image processing such as automatic target recognition.

Keywords

Multi-level thresholding Bat algorithm Otsu method Ikeda Map Peak signal to noise ratio (PSNR) Structural similarity index (SSIM) 

Notes

Compliance with ethical standards

Conflict of interest

We are the authors and confirm that there is no conflict of interest.

References

  1. 1.
    Ghosh SK (2012) Digital image processing. Narosa Publishing House Pvt. Ltd., New DelhiGoogle Scholar
  2. 2.
    Ghamisi P, Couceiro MS, Martins FML, Benediktsson JA (2014) Multilevel image segmentation based on fractional-order Darwinian particle swarm optimization. IEEE Trans Geosci Remote Sens 52(5):2382–2394CrossRefGoogle Scholar
  3. 3.
    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):1573–1601CrossRefGoogle Scholar
  4. 4.
    Manickavasagam K, Sutha S, Kamalanand K (2014) An automated system based on 2d empirical mode decomposition and k-means clustering for classification of Plasmodium species in thin blood smear images. BMC Infect Dis 14(Suppl 3):P13. doi: 10.1186/1471-2334-14-S3-P13 CrossRefGoogle Scholar
  5. 5.
    Manickavasagam K, Sutha S, Kamalanand K (2014) development of systems for classification of different plasmodium species in thin blood smear microscopic images. J Adv Microsc Res 9(2):86–92CrossRefGoogle Scholar
  6. 6.
    Kalyani M, Satapathy SC, Rao KR (2012) Artificial bee colony based image clustering, In: Proceedings of the international conference on information systems design and intelligent applications 2012 (INDIA 2012), Advances in Intelligent and Soft Computing, vol. 132, pp 29–37Google Scholar
  7. 7.
    Horng Ming-Huwi (2011) Multilevel thresholding selection based on the artificial bee colony algorithm for image segmentation. Expert Syst Appl 38(11):13785–13791Google Scholar
  8. 8.
    Sathya PD, Kayalvizhi R (2010) Optimum multilevel image thresholding based on tsallis entropy method with bacterial foraging algorithm. IJCSI Int J Comput Sci Issues 7(5):336–343Google Scholar
  9. 9.
    Dey N, Roy AB, Pal M, Das A (2012) FCM Based blood vessel segmentation method for retinal images, Int J Comput Sci Netw (IJCSN) 1(3) (ISSN 2277–5420)Google Scholar
  10. 10.
    Roy P, Goswami S, Chakraborty S, Azar AT, Dey N (2014) Image segmentation using rough set theory: a review. Int J Rough Sets Data Analy (IJRSDA) 1(2):62–74CrossRefGoogle Scholar
  11. 11.
    Pal G, Acharjee S, Rudrapaul D, Ashour AS, Dey N (2015) Video segmentation using minimum ratio similarity measurement. Int J Image Min 1(1):87–110Google Scholar
  12. 12.
    Samanta S, Dey N, Das P, Acharjee S, Chaudhuri SS, (2012) Multilevel threshold based gray scale image segmentation using cuckoo search, In: International conference on emerging trends in electrical, communication and information technologies -ICECIT, December 12–23Google Scholar
  13. 13.
    Samanta S, Acharjee S, Mukherjee A, Das D, Dey N (2013) Ant Weight Lifting Algorithm for Image Segmentation, In: 2013 IEEE international conference on computational intelligence and computing research (ICCIC), Madurai, December 26–28Google Scholar
  14. 14.
    Ghamisi P, Couceiro MS, Benediktsson JA, Ferreira NMF (2012) An efficient method for segmentation of images based on fractional calculus and natural selection. Expert Syst Appl 39(16):12407–12417CrossRefGoogle Scholar
  15. 15.
    Lee SU, Chung SY, Park RH (1990) A comparative performance study techniques for segmentation. Comput Vision Gr Image Process 52(2):171–190CrossRefGoogle Scholar
  16. 16.
    Sezgin M, Sankar B (2004) Survey over image thresholding techniques and quantitative performance evaluation. J Electron Imaging 13(1):146–165CrossRefGoogle Scholar
  17. 17.
    Pei JH, Xie WX (1999) Adaptive multi thresholds images segmentation based on fuzzy restrained histogram fcm clustering (in Chinese). Acta Electron Sin 27(10):38–42Google Scholar
  18. 18.
    Yen JC, Chang FJ, Chang S (1995) A new criterion for automatic multilevel thresholding. IEEE Trans Image Process 4(3):370–378CrossRefGoogle Scholar
  19. 19.
    Histogram TUTD, Principle FE (2000) xmin; if x < xmin xmax; if x > xmax x; otherwise. IEEE Trans Image Process 9(4):733Google Scholar
  20. 20.
    Manikantan K, Arun BV, Yaradonic DKS (2012) Optimal multilevel thresholds based on tsallis entropy method using golden ratio particle swarm optimization for improved image segmentation. Procedia Eng 30:364–371CrossRefGoogle Scholar
  21. 21.
    Akay Bahriye (2013) A study on particle swarm optimization and artificial bee colony algorithms for multilevel thresholding. Appl Soft Comput 13(6):3066–3091CrossRefGoogle Scholar
  22. 22.
    Rajinikanth V, Raja NSM, Latha K (2014) Optimal multilevel image thresholding: an analysis with PSO and BFO algorithms. Aust J Basic Appl Sci 8(9):443–454Google Scholar
  23. 23.
    Sathya PD, Kayalvizhi R (2011) Modified bacterial foraging algorithm based multilevel thresholding for image segmentation. Eng Appl Artif Intell 24:595–615CrossRefGoogle Scholar
  24. 24.
    Sathya PD, Kayalvizhi R (2011) Optimal multilevel thresholding using bacterial foraging algorithm. Expert Syst Appl 38:15549–15564CrossRefGoogle Scholar
  25. 25.
    Raja NSM, RajinikanthV, Latha K (2014) Otsu based optimal multilevel image thresholding using firefly algorithm, Model Simul Eng, vol. 2014, Article ID 794574, 17 pagesGoogle Scholar
  26. 26.
    Sarkar S, Das S (2013) Multilevel image thresholding based on 2D histogram and maximum tsallis entropy–a differential evolution approach. IEEE Trans Image Process 22(12):4788–4797MathSciNetCrossRefzbMATHGoogle Scholar
  27. 27.
    Charansiriphaisan K, Chiewchanwattana S, Sunat K (2014) A global multilevel thresholding using differential evolution approach, Math Probl Eng, vol. 2014, Article ID 974024, 23 pagesGoogle Scholar
  28. 28.
    Abhinaya B, Raja NSM (2015) Solving multi-level image thresholding problem—an analysis with cuckoo search algorithm. Inf Syst Design Intell Appl Adv Intell Syst Comput 339:177–186Google Scholar
  29. 29.
    Agrawal S, Panda R, Bhuyan S, Panigrahi BK (2013) Tsallis entropy based optimal multilevel thresholding using cuckoo search algorithm. Swarm Evol Comput 11:16–30CrossRefGoogle Scholar
  30. 30.
    Rajinikanth V, Aashiha JP, Atchaya A (2014) Gray-level histogram based multilevel threshold selection with bat algorithm. Int J Comput Appl 93(16):1–8Google Scholar
  31. 31.
    Rajinikanth V, Couceiro MS (2015) Optimal multilevel image threshold selection using a novel objective function. Inf Syst Design Intell Appl Adv Intell Syst Comput 340:177–186Google Scholar
  32. 32.
    Alihodzic A, Tuba M (2014) Improved bat algorithm applied to multilevel image thresholding, Sci World J, Vol. 2014, Article ID 176718, 16 pagesGoogle Scholar
  33. 33.
    Gao H, Xu W, Sun J, Tang Y (2010) Multilevel thresholding for image segmentation through an improved quantum-behaved particle swarm algorithm. IEEE Trans Instrum Meas 59(4):934–946CrossRefGoogle Scholar
  34. 34.
    Shah-Hosseini H (2013) Multilevel thresholding for image segmentation using the galaxy-based search algorithm. Int J Intell Syst Appl 5(11):19Google Scholar
  35. 35.
    Raja N, Rajinikanth V, Latha K (2014) Otsu based optimal multilevel image thresholding using firefly algorithm. Modell Simul Eng 2014:37Google Scholar
  36. 36.
    Yang XS (2008) Nature-inspired metaheuristic algorithms, 2nd edn. Luniver Press, FromeGoogle Scholar
  37. 37.
    Yang Xin-She (2013) Bat algorithm: literature review and applications. Int J Bio-Inspired Comput 5(3):141–149CrossRefGoogle Scholar
  38. 38.
    Otsu N (1979) A threshold selection method from gray-level histograms. IEEE Trans Syst Man Cybern 9(1):62–66MathSciNetCrossRefGoogle Scholar
  39. 39.
    Liao PS, Chen TS, Chung PC (2001) A fast algorithm for multi-level thresholding. J Inf Sci Eng 17(5):713–727Google Scholar
  40. 40.
    Manda Kalyani, Satapathy SC, Poornasatyanarayana B (2012) Population based meta-heuristic techniques for solving optimization problems: a selective survey. Int J Emerg Technol Adv Eng 2(11):206–211Google Scholar
  41. 41.
    Fister IJ, Yang XS, Fister I, Brest J, Fister D (2013) A brief review of nature-inspired algorithms for optimization. Electrotech Rev 80(3):1–7zbMATHGoogle Scholar
  42. 42.
    Yang XS, Deb S (2012) Two-stage eagle strategy with differential evolution. Int J Bio-Inspired Comput 4(1):1–5CrossRefGoogle Scholar
  43. 43.
    Gandomi AH, Yang XS, Talatahari S, Alavi AH (2013) Firefly algorithm with chaos. Commun Nonlinear Sci Numer Simul 18(1):89–98MathSciNetCrossRefzbMATHGoogle Scholar
  44. 44.
    Ikeda K (1979) Multiple-valued stationary state and its instability of the transmitted light by a ring cavity system. Opt Commun 30:257–261CrossRefGoogle Scholar
  45. 45.
    Ikeda K, Daido H, Akimoto O (1980) Optical turbulence: chaotic behavior of transmitted light from a ring cavity. Phys Rev Lett 45:709–712CrossRefGoogle Scholar
  46. 46.
    Alsing PM, Gavrielides A, Kovanis V (1994) Controlling unstable periodic orbits in a nonlinear optical system: the Ikeda map In: IEEE nonlinear optics: materials, fundamentals, and applications, NOL’94 IEEE, pp 72–74. doi: 10.1109/NLO.1994.470856
  47. 47.
    Paula ASD, Savi MA (2009) Controlling maps using an OGY multiparameter Chaos control method, In: 20th international congress of mechanical engineering, proceedings of COBEM 2009, November 15–20Google Scholar
  48. 48.
    Liao P-S, Chung P-C (2001) A fast algorithm for multilevel thresholding. J Inf Sci Eng 17(5):713–727Google Scholar
  49. 49.
    Wang Z, Bovik AC, Sheikh HR, Simoncelli EP (2004) Image quality assessment: from error measurement to structural similarity. IEEE Trans Image Process 13(1):1–14Google Scholar
  50. 50.
    Hore A, Ziou D (2010) Image quality metrics: Psnr vs. Ssim, In: IEEE international conference on pattern recognition (ICPR), Istanbul, Turkey, pp. 2366–2369Google Scholar
  51. 51.
  52. 52.
  53. 53.
    Moraru Luminita, Bibicu Dorin, Biswas Anjan (2013) Standalone functional CAD system for multi-object case analysis in hepatic disorders. Comput Biol Med 43(8):967–974CrossRefGoogle Scholar
  54. 54.
    Punga MV, Gaurav R, Moraru R (2014) Level set method coupled with energy image features for brain MR image segmentation. Biomed Eng/Biomedizinische Technik 59(3):219–229Google Scholar
  55. 55.
    Araki Tadashi, Ikeda Nobutaka, Dey Nilanjan, Chakraborty Sayan, Saba Luca, Kumar Dinesh, Godia Elisa Cuadrado et al (2015) A comparative approach of four different image registration techniques for quantitative assessment of coronary artery calcium lesions using intravascular ultrasound. Comput Methods Programs Biomed 118(2):158–172CrossRefGoogle Scholar
  56. 56.
    Ikeda Nobutaka, Gupta Ajay, Dey Nilanjan, Bose Soumyo, Shafique Shoaib, Arak Tadashi, Godia Elisa Cuadrado et al (2015) Improved correlation between carotid and coronary atherosclerosis SYNTAX score using automated ultrasound carotid bulb plaque IMT measurement. Ultrasound Med Biol 41(5):1247–1262CrossRefGoogle Scholar
  57. 57.
    Araki T, Ikeda D, Dey N, Acharjee S, Molinari F, Saba L, Godia EC, Nicolaides A, Suri JS (2015) Shape-based approach for coronary calcium lesion volume measurement on intravascular ultrasound imaging and its association with carotid intima-media thickness. J Ultrasound Med 34(3):469–482CrossRefGoogle Scholar
  58. 58.
    Araki T, Ikeda N, Molinari F, Dey N, Acharjee S, Saba L, Suri JS (2014) Link between automated coronary calcium volumes from intravascular ultrasound to automated carotid IMT from B-mode ultrasound in coronary artery disease population. Int Angiol 33(4):392–403Google Scholar
  59. 59.
    Ikeda N, Araki T, Dey N, Bose S, Shafique S, El-Baz A, Godia E, Cuadrado M, Anzidei L, Saba L, Suri JS (2014) Automated and accurate carotid bulb detection, its verification and validation in low quality frozen frames and motion video. Int Angiol 33(6):573–589Google Scholar
  60. 60.
    Araki T, Ikeda N, Molinari F, Dey N, Acharjee SM, Saba L, Nicolaides A, Suri JS (2014) Effect of geometric-based coronary calcium volume as a feature along with its shape-based attributes for cardiological risk prediction from low contrast intravascular ultrasound. J Med Imaging Health Inform 4(2):255–261CrossRefGoogle Scholar
  61. 61.
    Saba L, Dey N, Ashour AS, Samanta S, Nath SS, Chakraborty S, Chakraborty J, Kumar D, Marinho D, Suri JS (2016) Automated stratification of liver disease in ultrasound: an online accurate feature classification paradigm. Comput Methods Programs Biomed 130:118–134CrossRefGoogle Scholar
  62. 62.
    Virmani J, Dey N, Kumar V (2016) PCA-PNN and PCA-SVM based CAD systems for breast density classification. In: Applications of intelligent optimization in biology and medicine, pp. 159–180. Springer International Publishing, BerlinGoogle Scholar
  63. 63.
    Kausar N, Palaniappan S, Samir BB, Abdullah A, Dey N (2016) Systematic analysis of applied data mining based optimization algorithms in clinical attribute extraction and classification for diagnosis of cardiac patients. In: Applications of intelligent optimization in biology and medicine, pp. 217–231. Springer International Publishing, BerlinGoogle Scholar
  64. 64.
    Cheriguene S, Azizi N, Zemmal N, Dey N, Djellali H, Farah N (2016) Optimized tumor breast cancer classification using combining random subspace and static classifiers selection paradigms. In: Applications of intelligent optimization in biology and medicine, pp. 289–307. Springer International Publishing, BerlinGoogle Scholar

Copyright information

© The Natural Computing Applications Forum 2016

Authors and Affiliations

  • Suresh Chandra Satapathy
    • 1
  • N. Sri Madhava Raja
    • 2
  • V. Rajinikanth
    • 2
  • Amira S. Ashour
    • 3
    Email author
  • Nilanjan Dey
    • 4
  1. 1.Department of Computer Science and EngineeringAnil Neerukonda Institute of Technology and SciencesVisakhapatnamIndia
  2. 2.Department of Electronics and Instrumentation EngineeringSt. Joseph’s College of EngineeringChennaiIndia
  3. 3.Department of Electronics and Electrical Communications Engineering, Faculty of EngineeringTanta UniversityTantaEgypt
  4. 4.Department of Information TechnologyTechno India College of TechnologyKolkataIndia

Personalised recommendations