Skip to main content
SpringerLink
Log in

Optimal multilevel thresholding based on molecular kinetic theory optimization algorithm and line intercept histogram

  • Mechanical Engineering, Control Science and Information Engineering
  • Published:
Journal of Central South University Aims and scope Submit manuscript

Abstract

Among all segmentation techniques, Otsu thresholding method is widely used. Line intercept histogram based Otsu thresholding method (LIH Otsu method) can be more resistant to Gaussian noise, highly efficient in computing time, and can be easily extended to multilevel thresholding. But when images contain salt-and-pepper noise, LIH Otsu method performs poorly. An improved LIH Otsu method (ILIH Otsu method) is presented, which can be more resistant to Gaussian noise and salt-and-pepper noise. Moreover, it can be easily extended to multilevel thresholding. In order to improve the efficiency, the optimization algorithm based on the kinetic-molecular theory (KMTOA) is used to determine the optimal thresholds. The experimental results show that ILIH Otsu method has stronger anti-noise ability than two-dimensional Otsu thresholding method (2-D Otsu method), LIH Otsu method, K-means clustering algorithm and fuzzy clustering algorithm.

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

Similar content being viewed by others

References

  1. MAITRA M, CHATTERJEE A. A novel technique for multilevel optimal magnetic resonance brain image thresholding using bacterial foraging [J]. Measurement 2008, 41(10): 1124–1134.

    Article  Google Scholar 

  2. YU Zhi-wen, WANG Hua-san, WEN Gui-hua. A modified support vector machine and its application to image segmentation [J]. Image and Vision Computing 2011, 29(1): 29–40.

    Article  Google Scholar 

  3. HARALICK R M, SHAPIRO L G. Image segmentation techniques [J]. Computer Vision, Graphics, and Image Processing 1985, 29(1): 100–132.

    Article  Google Scholar 

  4. SAHOO P K, SOLTANI S, WONG A K C. A survey of threshol DING techniques [J]. Computer, Vision, Graphics and Image Processing 1988, 41(2): 233–260.

    Article  Google Scholar 

  5. SEZGIN M, SANKUR B. Survey over image threshol DING techniques and quantitative performance evaluation [J]. Journal of Electronic Imaging 2004, 13(1): 146–168.

    Article  Google Scholar 

  6. KUMAR S, PANT M, RAY A. Differential evolution embedded Otsu’s method for optimized image thresholding [C]// IEEE Conf Information and Communication Technologies. Mumbai: IEEE 2011: 325–329.

    Google Scholar 

  7. GAO Hao, XU Wen-bo, SUN Jun, TANG Yu-lan. Multilevel thresholding for image segmentation through an improved quantum-behaved particle swarm algorithm [J]. IEEE Transactions on Instrumentation and Measurement 2010, 59(4): 934–946.

    Article  Google Scholar 

  8. OTSU N. A threshold selection method from gray-level histograms [J]. IEEE Transactions on Systems, Man and Cybernetics 1979, SMC-9(1): 62–66.

    Google Scholar 

  9. LIU Jian-zhuang, LI Wen-qing. The automatic threshol DING of gray-level pictures via twodimensional Otsu method [J]. Acta Automatica Sinica 1993, 19(1): 101–105. (in Chinese)

    Google Scholar 

  10. HE Zhi-yong, SUN Li-ning, HUANG Wei-guo, CHEN Li-guo. Threshol DING segmentation algorithm based on Otsu criterion and line intercept histogram [J]. Optics and Precision Engineering 2012, 20(10): 2315–2323. (in Chinese)

    Article  Google Scholar 

  11. NIE Fang-yan, WANG Yong-lin, PAN Mei-sen, PENG Guang-hua, ZHANG Ping-feng. Two-dimensional extension of variance-based threshol DING for image segmentation [J]. Multidimensional System and Signal Processing 2013, 24(3): 485–501.

    Article  MathSciNet  MATH  Google Scholar 

  12. YIN Peng-yeng, CHEN Ling-hwei. A fast iterative scheme for multilevel threshol DING methods [J]. Signal Process 1997, 60(3): 305–313.

    Article  MathSciNet  Google Scholar 

  13. HORNG M H. Multilevel threshol DING selection based on the artificial bee colony algorithm for image segmentation [J]. Expert Systems with Applications. 2011, 38(11): 13785–13791.

    Google Scholar 

  14. CHANDER A, CHATTERJEE A, SIARRY P. A new social and momentum component adaptive PSO algorithm for image segmentation [J]. Expert Systems with Applications 2011, 38(5): 4998–5004.

    Article  Google Scholar 

  15. XU Xing, LI Yuang-xiang, JIANG Da-zhi, TANG Ming-duan, FANG Shen-lin. Improved particle swarm optimization algorithm based on theory of molecular motion [J]. Journal of System Simulation 2009, 21(7): 1904–1907. (in Chinese)

    Google Scholar 

  16. CHEN Dong-ning, YAO Cheng-yu, WANG Bin, ZHANG Rui-xing. LRPSO algorithm and applications in reliability optimization [J]. China Mechanical Engineering 2014, 25(21): 2930–2936. (in Chinese)

    Google Scholar 

  17. FAN Cheng-li, XING Qing-hua, LI Xiang, WANG Zhen-jiang. Improved particle swarm optimization algorithm with reverse forecast and repulsion [J]. Control and Decision 2015, 30(2): 311–315. (in Chinese)

    Google Scholar 

  18. FAN Chao-dong, OUYANG Hong-lin, ZHANG Ying-jie, AI Zhao-yang. Optimization algorithm based on kinetic-molecular theory [J]. Journal of Central South University 2013, 20(12): 3504–3512.

    Article  Google Scholar 

  19. ZHANG Jun, HU Jing-lu. Image segmentation based on 2D Otsu method with histogram analysis [C]// IEEE Conf Computer Science and Software Engineering. Washington DC: IEEE Computer Society 2008: 105–108.

    Google Scholar 

  20. SAHOO P K, SLAAF D W, ALBERT T A. Threshold selection using a minimal histogram entropy difference [J]. Optical Engineering 1997, 36(7): 1976–1981.

    Article  Google Scholar 

  21. FAN Jiu-lun, ZHAO Feng. Two-dimensional Otsu’s curve thresholding segmentation method for gray-level images [J]. Acta Electronica Sinica 2007, 35(4): 751–755. (in Chinese)

    Google Scholar 

  22. WU Yi-quan, PAN Zhe, WU Wen-yi. Image threshol DING based on two-dimensional histogram oblique segmentation and its fast recurring algorithm [J]. Journal on Communication. 2008, 29(4): 77–83. (in Chinese)

    MathSciNet  Google Scholar 

  23. HAN Xu-ming, ZUO Wang-li, WANG Li-min, SHI Xiao-hu. Atmospheric quality assessment model based on immune algorithm optimization and its applications [J]. Journal of Computer Research and Development 2011, 48(7): 1307–1313. (in Chinese)

    Google Scholar 

  24. DEB K, PRATAP A, AGARWAL S, MCYARIVAN T. A fast and elitist multiobjective genetic algorithm: NSGA-II [J]. IEEE Transactions on Evolutionary Computation 2002, 6(2): 182–197.

    Article  Google Scholar 

  25. USC-SIPI Image Database. [Online] [2014]. http://sipi.usc.edu/ database/database.php.

  26. GHAMISI P, COUCEIRO M S, BENEDIKTSSON J A, FERREIRA N F. An efficient method for segmentation of images based on fractional calculus and natural selection [J]. Expert Systems with Applications 2012, 39(16): 12407–12417.

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Key Laboratory of Intelligent Computing and Information Processing, Xiangtan University, Xiangtan, 411105, China

    Chao-dong Fan  (范朝冬), Ke Ren  (任柯) & Ling-zhi Yi  (易灵芝)

  2. Hunan Province Cooperative Innovation Center for Wind Power Equipment and Energy Conversion, Xiangtan, 411101, China

    Chao-dong Fan  (范朝冬), Ke Ren  (任柯) & Ling-zhi Yi  (易灵芝)

  3. College of Information Science and Engineering, Hunan University, Changsha, 410082, China

    Ying-jie Zhang  (张英杰)

Authors
  1. Chao-dong Fan  (范朝冬)
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Ke Ren  (任柯)
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Ying-jie Zhang  (张英杰)
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Ling-zhi Yi  (易灵芝)
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Ling-zhi Yi  (易灵芝).

Additional information

Foundation item: Project(61440026) supported by the National Natural Science Foundation of China; Project(11KZ KZ08062) supported by Doctoral Research Project of Xiangtan University, China

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Fan, Cd., Ren, K., Zhang, Yj. et al. Optimal multilevel thresholding based on molecular kinetic theory optimization algorithm and line intercept histogram. J. Cent. South Univ. 23, 880–890 (2016). https://doi.org/10.1007/s11771-016-3135-8

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11771-016-3135-8

Key words

Search

Navigation