Advertisement

Adaptive Threshold Processing of Secondary Electron Images in Scanning Electron Microscope

  • Weiguo Bian
  • Mingyu Wang
  • Zhan YangEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11740)

Abstract

Observing the sample under a scanning electron microscope (SEM) requires adjustment of brightness and contrast to obtain a clear image. The traditional method is manually adjusted by the operator, which inevitably has errors. In this paper, an adaptive threshold processing method based on image-based normalized gray histogram is proposed. This method can acquire the threshold of the image according to the state of the currently obtained secondary electron images. When the brightness and contrast of the image change, the threshold can also be changed accordingly. It is concluded from a large number of tests that when the secondary electron image gray histogram has obvious double peaks and is located in the trough, the threshold obtained is optimal. Therefore, it is possible to better observe the pictures under the SEM.

Keywords

SEM Secondary electron images Adaptive threshold Gray histogram Binarization 

Notes

Acknowledgments

This work is supported by National Key R&D Program of China (2018YFB1304901).

References

  1. 1.
    Reimer, L.: Scanning electron microscopy. Cirp Encycl. Prod. Eng. 94(6), 756–776 (1985)Google Scholar
  2. 2.
    Jackman, H., Krakhmalev, P., Svensson, K.: Image formation mechanisms in scanning electron microscopy of carbon nanotubes, and retrieval of their intrinsic dimensions. Ultramicroscopy 124(1), 35–39 (2013)CrossRefGoogle Scholar
  3. 3.
    Cazaux, J.: From the physics of secondary electron emission to image contrasts in scanning electron microscopy. J. Electron Microsc. 61(5), 261–284 (2012)CrossRefGoogle Scholar
  4. 4.
    Würtz, P., Gericke, T., Vogler, A., et al.: Image formation in scanning electron microscopy of ultracold atoms. Appl. Phys. B: Lasers Opt. 98(4), 641–645 (2010)CrossRefGoogle Scholar
  5. 5.
    Midoh, Y., Miura, K., Nakamae, K., et al.: Statistical optimization of Canny edge detector for measurement of fine line patterns in SEM image. Meas. Sci. Technol. 16(2), 477–487 (2005)CrossRefGoogle Scholar
  6. 6.
    Pratt, W.K.: Digital Image Processing. Wiley-Interscience, Hoboken (1978)zbMATHGoogle Scholar
  7. 7.
    Kundu, S., Jana, P., De, D., et al.: SEM image processing of polymer nanocomposites to estimate filler content. In: IEEE International Conference on Electrical. IEEE (2015)Google Scholar
  8. 8.
    Galloway, J.A., Montminy, M.D., Macosko, C.W.: Image analysis for interfacial area and cocontinuity detection in polymer blends. Polymer 43(17), 4715–4722 (2002)CrossRefGoogle Scholar
  9. 9.
    Li, D., Wang, Y.: Application of an improved threshold segmentation method in SEM material analysis (2018)Google Scholar
  10. 10.
    Wang, Z., Wang, Y., Jiang, L., et al.: An image segmentation method using automatic threshold based on improved genetic selecting algorithm. Autom. Control Comput. Sci. 50(6), 432–440 (2016)CrossRefGoogle Scholar
  11. 11.
    Lei, Y.Y., Wang, R., Yao, J.M., et al.: Research and implementation on image denoising for scanning electron microscopy. Opt. Optoelectron. Technol. 12(5), 77–82 (2014)Google Scholar
  12. 12.
    David, S., Visvikis, D., Roux, C., et al.: Multi-observation PET image analysis for patient follow-up quantitation and therapy assessment. Phys. Med. Biol. 56(18), 5771–5788 (2011)CrossRefGoogle Scholar
  13. 13.
    Chao, X., Fenghua, H., Zhengyuan, M.: An improved two-dimensional Otsu thresholding segmentation method. Appl. Electron. Tech. (2016)Google Scholar
  14. 14.
    Wang, Y.Q., Zhuang, L.L., Shi, C.X.: Construction research on multi-threshold segmentation based on improved Otsu threshold method. Adv. Mater. Res. 1046, 425–428 (2014)CrossRefGoogle Scholar
  15. 15.
    Ni, L., Kailin, P., Yexiang, N.: An improved automatic threshold segmentation method used in PCBA vision inspection. In: Second International Symposium on Test Automation & Instrumentation (2008)Google Scholar
  16. 16.
    Solomon, A., Cassuto, Y.: Adaptive threshold read algorithms in multi-level non-volatile memories with uncertainty. In: Science of Electrical Engineering (2017)Google Scholar
  17. 17.
    Yang, Y., Li, X., Pan, X., et al.: Downscaling land surface temperature in complex regions by using multiple scale factors with adaptive thresholds. Sensors 17(4), 744 (2017)CrossRefGoogle Scholar
  18. 18.
    Ting, D.: Adaptive threshold sampling and estimation (2017)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.School of Mechanical and Electric EngineeringSoochow UniversitySuzhouChina

Personalised recommendations