Skip to main content
SpringerLink
Log in

A method for improving the estimation accuracy of the particle size distribution of the minerals using image analysis

  • Published:
Computational Particle Mechanics Aims and scope Submit manuscript

Abstract

There are several methods for measuring the particle size distribution of the comminuted minerals, but the most used in situ analysis methods are sieving and microscopic analysis due to the particle size characteristics. This paper presents the improved method of the microscopic image analysis to improve the accuracy estimating the particle size distribution of comminuted minerals. The image processing methods we used are microscopic image stitching, individual particle segmentation with watershed and deep learning and individual particle weight estimation with shape factor. We experimented with three samples, namely ferruginous quartzite, coal and magnetite, for estimation of the particle size distribution, and confirmed to improve the accuracy estimation through comparison with sieving test.

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

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16
Fig. 17
Fig. 18

Similar content being viewed by others

References

  1. Bernhardt C (1994) Particle size analysis: classification and sedimentation methods. New Delhi. Thomson Press Ltd

    Google Scholar 

  2. Fleming BW, Thum AB (1978) The settling tube-a hydraulic method for grain size analysis of sands. Kieler Meeresforschungen 4(1):82–95

  3. Gibbs RJ (1974) A settling tube system for sand-size analysis. J Sediment Res 44(2):583–588

    Article  Google Scholar 

  4. Poole DM (1957) Size analysis of sand by a sedimentation technique. J Sediment Res 27(4):460–468

    Google Scholar 

  5. Beuselinck L (1998) Grain-size analysis by laser diffractometry: comparison with the sieve-pipette method. CATENA 32(3–4):193–208

    Article  Google Scholar 

  6. Konert M, Vandenberghe J (1997) Comparison of laser grain size analysis with pipette and sieve analysis: a solution for the underestimation of the clay fraction. Sedimentology 44(3):523–535

    Article  Google Scholar 

  7. Zhang ZL, Yang JG, Ding LH, Zhao YM (2012) An improved estimation of coal particle mass using image analysis. Powder Technol 229:178–184

    Article  Google Scholar 

  8. Kursun I (2009) Particle size and shape characteristics of kemerburgaz quartz sands obtained by sieving laser diffraction and digital image processing methods. Miner Process Extr Metall Rev 30(4):346–360

    Article  Google Scholar 

  9. Coster M, Chermant JL (2001) Image analysis and mathematical morphology for civil engineering materials. Cem Concr Compos 23(2–3):133–151

    Article  Google Scholar 

  10. Marinoni N, Pavese A, Foi M, Trombino L (2005) Characterisation of mortar morphology in thin sections by digital image processing. Cem Concr Res 35(8):1613–1619

    Article  Google Scholar 

  11. Wang WX (2006) Size and shape measure of particles by image analysis. In: IWCIA 2006: combinatorial image analysis pp 253–262

  12. Al-Rousan T, Masad E, Tutumluer E, Pan T (2007) Evaluation of image analysis techniques for quantifying aggregate shape characteristics. Constr Build Mater 21(5):978–990

    Article  Google Scholar 

  13. Kumara Janaka J, Hayano K, Ogiwara K (2012) Image analysis techniques on evaluation of particle size distribution of gravel. Int J GEOMATE 3(1):290–297

    Google Scholar 

  14. Podczeck F (1997) A shape factor to assess the shape of particles using image analysis. Powder Technol 93:47–53

    Article  Google Scholar 

  15. Olson E (2011) Particle shape factors and their use in image analysis—part 1: theory. J GXP Compliance 15(3):85–96

    Google Scholar 

  16. Stanley-Wood NG, Lines RW (2007) Particle size analysis. Royal Society of Chemistry, London

  17. Lowe DG (2004) Distinctive image features from scale-invariant keypoints. Int J Comput Vision 60(2):91–110

    Article  Google Scholar 

  18. Bay H, Ess A, Tuytelaars T, Gool LV (2008) Speeded-up robust features (SURF). Comput Vis Image Underst 110(3):346–359

    Article  Google Scholar 

  19. Ro SH, Kim SH (2021) An image stitching algorithm for the mineralogical analysis. Miner Eng 169:106968

    Article  Google Scholar 

  20. Beare R (2005) Efficient implementation of the locally constrained watershed transform and seeded region growing. mathematical morphology: 40 Years On: pp 217–226

  21. Landeen T and Gunther J (2018) Deep learning based image overlap detection. https://digitalcommons.usu.edu/cgi/viewcontent.cgi?article=1510&context=spacegrant, Google scholar

  22. Sun HQ, Luo YJ (2009) Adaptive watershed segmentation of binary particle image. J Microsc 233:326–330

    Article  MathSciNet  Google Scholar 

  23. Teng L, Li H, Karim S (2019) DMCNN: a deep multiscale convolutional neural network model for medical image segmentation. J Healthc Eng 1–10:8597606

    Google Scholar 

  24. Zhou Y, Gao Q, Wang Y, Zhou K (2014) Systematical improvements of trace gold field rapid determination method. Geophys Geochem Explor 3:539–543

Download references

Acknowledgments

This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.

Author information

Authors and Affiliations

  1. Institute of Ferrous Mining, State Academy of Sciences, Pyongyang, 999093, Democratic People’s Republic of Korea

    SungHyok Ro

  2. Faculty of Information Engineering, Pyongyang University of Mechanical Engineering, Pyongyang, 999093, Democratic People’s Republic of Korea

    JinHyok Jon

  3. School of Land and Environment Protection, Pyongyang University of Architecture, Pyongyang, 999093, Democratic People’s Republic of Korea

    KumSong Ryu

Authors
  1. SungHyok Ro
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. JinHyok Jon
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. KumSong Ryu
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to JinHyok Jon.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Ro, S., Jon, J. & Ryu, K. A method for improving the estimation accuracy of the particle size distribution of the minerals using image analysis. Comp. Part. Mech. 10, 929–941 (2023). https://doi.org/10.1007/s40571-022-00538-x

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s40571-022-00538-x

Keywords

Search

Navigation