Table of Contents Author Guidelines Submit a Manuscript
Mathematical Problems in Engineering
Volume 2014, Article ID 932695, 13 pages
http://dx.doi.org/10.1155/2014/932695
Research Article

Minimum Error Thresholding Segmentation Algorithm Based on 3D Grayscale Histogram

1School of Software, Jiangxi Normal University, Nanchang 330022, China
2Department of Computer Application, Faculty of Information Science and Technology, Southwest Jiaotong University, Chengdu 610031, China
3Department of Electronic & Information Engineering, Faculty of Electrical Engineering, Southwest Jiaotong University, Chengdu 610031, China

Received 25 October 2013; Revised 5 December 2013; Accepted 9 December 2013; Published 14 January 2014

Academic Editor: Su-Qun Cao

Copyright © 2014 Jin Liu et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Linked References

  1. S. Candemir and Y. S. Akgül, “Statistical significance based graph cut regularization for medical image segmentation,” Turkish Journal of Electrical Engineering & Computer Sciences, vol. 19, no. 6, pp. 957–972, 2011. View at Publisher · View at Google Scholar · View at Scopus
  2. M. Ceylan, Y. Özbay, O. N. Uçan, and E. Yildirim, “A novel method for lung segmentation on chest CT images: complex-valued artificial neural network with complex wavelet transform,” Turkish Journal of Electrical Engineering & Computer Sciences, vol. 18, no. 4, pp. 613–623, 2010. View at Publisher · View at Google Scholar · View at Scopus
  3. A. M. Vil'kin, I. V. Safonov, and M. A. Egorova, “Bottom-up document segmentation method based on textural features,” Pattern Recognition and Image Analysis, vol. 21, no. 3, pp. 565–568, 2011. View at Publisher · View at Google Scholar · View at Scopus
  4. K. H. Hsia, S. F. Lien, and J. P. Su, “Fast restoration of warped document image based on text rectangle area segmentation,” Journal of Software, vol. 8, pp. 1162–1167, 2013. View at Google Scholar
  5. P. Nadia and T. Sinisa, “Hough forest random field for object recognition and segmentation,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 35, pp. 1066–1079, 2013. View at Google Scholar
  6. M.-D. Yang, T.-C. Su, N.-F. Pan, and Y.-F. Yang, “Systematic image quality assessment for sewer inspection,” Expert Systems with Applications, vol. 38, no. 3, pp. 1766–1776, 2011. View at Publisher · View at Google Scholar · View at Scopus
  7. J. J. Ranjani and S. J. Thiruvengadam, “Fast threshold selection algorithm for segmentation of synthetic aperture radar images,” IET Radar, Sonar & Navigation, vol. 6, no. 8, pp. 788–795, 2012. View at Publisher · View at Google Scholar
  8. M. K. Quweider, J. D. Scargle, and B. Jackson, “Grey level reduction for segmentation, threshholding and binarisation of images based on optimal partitioning on an interval,” IET Image Processing, vol. 1, no. 2, pp. 103–111, 2007. View at Publisher · View at Google Scholar · View at Scopus
  9. D. Ahmed, H. Kamal, and D. Moussa, “Fast multilevel thresholding for image segmentation through a multiphase level set method,” Signal Processing, vol. 93, pp. 139–153, 2013. View at Google Scholar
  10. N. Otsu, “A threshold selection method from gray-level histograms,” IEEE Transactions on Systems, Man, and Cybernetics, vol. 9, no. 1, pp. 62–66, 1979. View at Google Scholar · View at Scopus
  11. J. Kittler and J. Illingworth, “Minimum error thresholding,” Pattern Recognition, vol. 19, no. 1, pp. 41–47, 1986. View at Google Scholar · View at Scopus
  12. J. N. Kapur, P. K. Sahoo, and A. K. C. Wong, “A new method for gray-level picture thresholding using the entropy of the histogram,” Computer Vision, Graphics, & Image Processing, vol. 29, no. 3, pp. 273–285, 1985. View at Google Scholar · View at Scopus
  13. M. Sezgin and B. Sankur, “Survey over image thresholding techniques and quantitative performance evaluation,” Journal of Electronic Imaging, vol. 13, no. 1, pp. 146–168, 2004. View at Publisher · View at Google Scholar · View at Scopus
  14. M.-H. Horng and R.-J. Liou, “Multilevel minimum cross entropy threshold selection based on the firefly algorithm,” Expert Systems with Applications, vol. 38, no. 12, pp. 14805–14811, 2011. View at Publisher · View at Google Scholar · View at Scopus
  15. A. Nakib, H. Oulhadj, and P. Siarry, “Image histogram thresholding based on multiobjective optimization,” Signal Processing, vol. 87, no. 11, pp. 2516–2534, 2007. View at Publisher · View at Google Scholar · View at Scopus
  16. P. Gu and Y. Zhang, “Improved 2D Otsu segmentation algorithm for infrared image,” Journal of Image and Graphics, vol. 16, no. 8, pp. 1424–1428, 2011. View at Google Scholar
  17. X. Y. Yan and L. C. Jiao, “Non-local three dimensional Otsu image thresholding segmentation based on anisotropic adaptive Gaussian weighted window,” Journal of Electronics & Information Technology, vol. 34, pp. 2672–2679, 2012. View at Google Scholar
  18. S. Kullback, Information Theory and Statistics, Dover, Mineola, NY, USA, 1968.
  19. S. P. Foliquet and L. Guigues, “Evaluation of image segmentation: state of the art, new criteria and comparison,” Traitement du Signal, vol. 23, pp. 109–124, 2003. View at Google Scholar
  20. M. D. Levine and A. M. Nazif, “Dynamic measurement of computer generated image segmentations,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 7, no. 2, pp. 155–164, 1985. View at Google Scholar · View at Scopus
  21. C. Rosenberger, Mise en oeuvre d'un system adaptatif de segmentation d'image [Ph.D. thesis], University of Rennes I, Rennes, France, 1999.