Adaptive analysis method for particles image

  • Wencheng WangEmail author
  • Tao Ji


To address the adhesion problem that usually exists in particle image analysis, a kind of adaptive method is proposed in this paper. First, gray transformation and median filtering are conducted on a particle image. Then, the obtained target image is threshold segmented. In addition, distance transformation and watershed segmentation are performed on the binary image that was processed based on the mathematical morphology, and the watershed ridge line in the image can be obtained. The boundary in the adhesion region is extracted by performing an intersection calculation between the original image and the segmented target area. Finally, the parameters of single particles, such as the area, perimeter and particle diameter, are calculated, and particle image analysis is realized. Through experiments on images collected in the laboratory, it is shown that this method is simple and convenient and can be popularized in the industry.


Adhesive particles Mathematic morphology Overlapping particles Watershed method 



This work is supported by National Nature Science Foundation of China (Nos. 61403283, 61876099), Shandong Provincial Natural Science Foundation, China (No.ZR2013FQ036) and Technology Development Plan of Weifang City (No.201301015). We are grateful to Dr. Zhenxue Chen for helping us to process the technical editing of the manuscript.


  1. 1.
    Cloppet F, Boucher A (2010) Segmentation of complex nucleus configurations in biological images. Pattern Recogn Lett 31(8):755–761CrossRefGoogle Scholar
  2. 2.
    Farhan M, Yli-Harja O, Niemistö A (2013) A novel method for splitting clumps of convex objects incorporating image intensity and using rectangular window-based concavity point-pair search. Pattern Recogn 46(3):741–751CrossRefGoogle Scholar
  3. 3.
    Karvelis P, Likas A, Fotiadis DI (2010) Identifying touching and overlapping chromosomes using the watershed transform and gradient paths. Pattern Recogn Lett 31(16):2474–2488CrossRefGoogle Scholar
  4. 4.
    Kharma N, Moghnieh H, Yao J, Guo YP, Abu-Baker A, Laganiere J, Rouleau G, Cheriet M (2007) Automatic segmentation of cells from microscopic imagery using ellipse detection. IET Image Process 1(1):39–47CrossRefGoogle Scholar
  5. 5.
    Lin C, Pun CM, Huang G (2016) Highly non-rigid video object tracking using segment-based object candidates. Multimed Tools Appl 76(7):9565–9586CrossRefGoogle Scholar
  6. 6.
    Long X, Cleveland WL, Yao YL (2010) Multiclass detection of cells in multi-contrast composite images. Comput Biol Med 40(2):168–178CrossRefGoogle Scholar
  7. 7.
    Lu Y (2008) Study for automatic grain insect counting system based on image processing. Microcomput Inform 23(8):311–312Google Scholar
  8. 8.
    Mukherjee DP, Potapovich Y, Levner I, Zhang H (2009) Ore image segmentation by learning image and shape features. Pattern Recogn Lett 30(6):615–622CrossRefGoogle Scholar
  9. 9.
    Nazlibilek S, Karacor D, Ercan T, Sazli MH, Kalender O, Ege Y (2014) Automatic segmentation, counting, size determination and classification of white blood cells. Measurement 55:58–65CrossRefGoogle Scholar
  10. 10.
    Ren J, Chen G, Li X, Mao K (2017) Striped-texture image segmentation with application to multimedia security. Multimed Tools Appl (11), 1–14Google Scholar
  11. 11.
    Ruberto CD, Dempster A, Khan S, Jarra B (2002) Analysis of infected blood cell images using morphological operators. Image Vis Comput 20(2):133–146CrossRefGoogle Scholar
  12. 12.
    Salinas RA, Raff U, Farfan C (2005) Automated estimation of rock fragment distributions using computer vision and its application in mining. IEEE Proc-vision Image Signal Process 152(1):1–8CrossRefGoogle Scholar
  13. 13.
    Schmitt O, Hasse M (2009) Morphological multiscale decomposition of connected regions with emphasis on cell clusters. Comput Vis Image Underst 113(2):188–201CrossRefGoogle Scholar
  14. 14.
    Su N, Xue HR (2009) The segmentation of overlapping Milk somatic cells based on improved watershed algorithm. Int Conf Artif Intell Comput Intell: 563–566Google Scholar
  15. 15.
    Wang W, Wang Y, Ji T (2012) Grains automatic counting method based on computer version. Int J Advan Comput Technol 4(3):345–351Google Scholar
  16. 16.
    Wang W, Yuan X, Wu X, Liu Y (2017) Fast image dehazing method based on linear transformation. IEEE Trans Multimed 19(6):1142–1155CrossRefGoogle Scholar
  17. 17.
    Wang W, Yuan X, Wu X, Ji T, Feng L (2018) Seperating touching particles: a concavity-based method using the area ratio of a circular mask. IEEE Syst Man Cybernet Mag 4(2):24–32CrossRefGoogle Scholar
  18. 18.
    Yang H, Ahuja N (2014) Automatic segmentation of granular objects in images: combining local density clustering and gradient-barrier watershed. Pattern Recogn 47(6):2266–2279CrossRefGoogle Scholar
  19. 19.
    Yang Z, Jia D, Ioannidis S, Mi N, Sheng B (2018) Intermediate data caching optimization for multi-stage and parallel big data frameworks. 2018 IEEE Int Conf Cloud ComputGoogle Scholar
  20. 20.
    Yang Z, Wang Y, Bhamini J, Tan C, Mi N (2018) Ead: elasticity aware deduplication manager for datacenters with multi-tier storage systems. Clust Comput (12), 1–19Google Scholar
  21. 21.
    Zhao X, Jia X (2017) A grain particle counting method based on matlab. J Henan Instit Sci Technol 45(5):65–69MathSciNetGoogle Scholar
  22. 22.
    Zhao J, Wang X, Yan H (2017) Method of classification for touching wolfberry based on watershed and regional area weighted. Trans Microsyst Technol 36(9):49–52Google Scholar
  23. 23.
    Zhou Y, Zeng LB, Liu JT (2003) A method for automatic colony counting based on image processing and its realization. J Data Acquis Process 18(4):460–464Google Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.College of Information and Control EngineeringWeifang UniversityWeifangChina

Personalised recommendations