Journal of Biosystems Engineering

Korean Society for Agricultural Machinery (한국농업기계학회)
권호 표지
DOI QR코드

DOI QR Code

Multi-Level Thresholding based on Non-Parametric Approaches for Fast Segmentation

  • Cho, Sung Ho (Department of Bio-Mechatronic Engineering, College of Biotechnology & Bioengineering, Sungkyunkwan University) ;
  • Duy, Hoang Thai (Department of Bio-Mechatronic Engineering, College of Biotechnology & Bioengineering, Sungkyunkwan University) ;
  • Han, Jae Woong (Division of Bio-Industry Engineering, Koungju National University) ;
  • Hwang, Heon (Department of Bio-Mechatronic Engineering, College of Biotechnology & Bioengineering, Sungkyunkwan University)
  • Received : 2013.03.01
  • Accepted : 2013.05.31
  • Published : 2013.06.01
Download PDF
⟨ Previous Next ⟩

Abstract

Purpose: In image segmentation via thresholding, Otsu and Kapur methods have been widely used because of their effectiveness and robustness. However, computational complexity of these methods grows exponentially as the number of thresholds increases due to the exhaustive search characteristics. Methods: Particle swarm optimization (PSO) and genetic algorithms (GAs) can accelerate the computation. Both methods, however, also have some drawbacks including slow convergence and ease of being trapped in a local optimum instead of a global optimum. To overcome these difficulties, we proposed two new multi-level thresholding methods based on Bacteria Foraging PSO (BFPSO) and real-coded GA algorithms for fast segmentation. Results: The results from BFPSO and real-coded GA methods were compared with each other and also compared with the results obtained from the Otsu and Kapur methods. Conclusions: The proposed methods were computationally efficient and showed the excellent accuracy and stability. Results of the proposed methods were demonstrated using four real images.

Keywords

References

  1. Albuquerque, M. P., I. A. Esquef and A. R. G. Mello. 2004. Image thresholding using Tsaillis entropy. Pattern Recognition letters 25(9):1059-1065. https://doi.org/10.1016/j.patrec.2004.03.003
  2. Barron, U. G and F. Butler. 2006. A comparison of seven thresholding techniques with the k-means clustering algorithm for measurement of bread-crumb features by digital image analysis. Journal of Food Engineering 74:268-278. https://doi.org/10.1016/j.jfoodeng.2005.03.007
  3. Brink, A. D and N. E. Pendock. 1996. Minimum cross-entropy threshold selection. Pattern Recognition 29(1):179-188. https://doi.org/10.1016/0031-3203(95)00066-6
  4. Cao, L., P. Bao and Z. Shi. 2008. The strongest schema learning GA and its application to multilevel thresholding. Image and Vision Computing 26:716-724. https://doi.org/10.1016/j.imavis.2007.08.007
  5. Chengxin, Y., N. Sang and T. Zhang. 2003. Local entropybased transition region extraction and thresholding. Pattern Recognition letters 24:2935-2941. https://doi.org/10.1016/S0167-8655(03)00154-5
  6. Cheng, H. D., J. R. Chen and J. G. Li. 1998. Threshold selection based on fuzzy c-Partition entropy approach. Pattern Recognition 31(7):857-870. https://doi.org/10.1016/S0031-3203(97)00113-1
  7. Das, S., A. Abraham and S. K. Sarkar. 2006. A Hybrid Rough Set-Particle Swarm Algorithm for Image Pixel Classification. Hybrid Intelligent Systems, HIS '06. Sixth International Conference on.
  8. Fan, S. K. S and Y. Lin. 2007. A multi-level thresholding approach using a hybrid optimal estimation algorithm. Pattern Recognition Letters 28:662-669. https://doi.org/10.1016/j.patrec.2006.11.005
  9. Hammouche, K., M. Diaf and P. Siarry. 2008. A multilevel automatic thresholding method based on a genetic algorithm for a fast image segmentation. Computer Vision and Image Understanding 109:163-175. https://doi.org/10.1016/j.cviu.2007.09.001
  10. Kapur, J. N., P. K. Sahoo and A. K. C. Wong. 1985. A new method for gray level picture thresholding using the entropy of the histogram. Computing Vision Graphics Image Process 29(3):273-285. https://doi.org/10.1016/0734-189X(85)90125-2
  11. Li, L and D. Li. 2008. Fuzzy entropy image segmentation based on particle swarm optimization. Progress in Natural Science 18:1167-1171. https://doi.org/10.1016/j.pnsc.2008.03.020
  12. Lievers, W. B and A. K. Pilkey. 2004. An evaluation of global thresholding techniques for the automatic image segmentation of automotive aluminum sheet alloys. Materials Science and Engineering A 381:134-142. https://doi.org/10.1016/j.msea.2004.04.002
  13. Maitra, M and A. Chatterjee. 2008. A hybrid cooperativecomprehensive learning based PSO algorithm for image segmentation using multilevel thresholding, Expert Systems with Applications 34(2):1341-1350. https://doi.org/10.1016/j.eswa.2007.01.002
  14. Mishra, S. 2005. A hybrid least square-fuzzy bacterial foraging strategy for harmonic estimation, IEEE Transactions on Evolutionary Computation 9(1):61-73. https://doi.org/10.1109/TEVC.2004.840144
  15. Niknam, T and B. Amiri. 2010. An efficient hybrid approach based on PSO, ACO and k-means for cluster analysis. Applied Soft Computing 10:183-197. https://doi.org/10.1016/j.asoc.2009.07.001
  16. Otsu, N. 1997. A threshold selection method from grey level histogram. IEEE Transactions on Systems, Man, and Cybernetics. SMC-9(1):62-66.
  17. Passino, K. M. 2002. Biomimicry of bacterial foraging for distributed optimization and control. Control Systems Magazine. IEEE 22(3):52-67. https://doi.org/10.1109/MCS.2002.1004010
  18. Sahoo, P., C. Wilkins and J. Yeager. 1997. Threshold seclection using Renyi's entropy. Pattern Recognition 30(1): 71-84. https://doi.org/10.1016/S0031-3203(96)00065-9
  19. Scheunders, P. 1997. A genetic c-means clustering algorithm applied to color image quantization. Pattern Recognition 30(6):859-866. https://doi.org/10.1016/S0031-3203(96)00131-8
  20. Tao, W. B., J. W. Tian and J. Liu. 2003. Image segmentation by three-level thresholding based on maximum fuzzy entropy and genetic algorithm. Pattern Recognition Letters 24:3069-3078. https://doi.org/10.1016/S0167-8655(03)00166-1
  21. Yin, P. Y. 1999. A fast scheme for optimal thresholding using genetic algorithms. Signal Processing 72:85-95. https://doi.org/10.1016/S0165-1684(98)00167-4
  22. Zahara, E., S. K. S. Fan and D. M. Tsai. 2005. Optimal multi-thresholding using a hybrid optimization approach, Pattern Recognition Letters 26(8):1082-1095. https://doi.org/10.1016/j.patrec.2004.10.003

Cited by

  1. Noninvasive Glucose Monitoring with a Contact Lens and Smartphone vol.18, pp.10, 2018, https://doi.org/10.3390/s18103208