(44-3) 07 * << * >> * Russian * English * Content * All Issues

An efficient algorithm for overlapping bubbles segmentation
Afef Bettaieb 1, Nabila Filali 1, Taoufik Filali 1, Habib Ben Aissia 1

Laboratory of Metrology and Energetic Systems, National School of Engineers of Monastir,
University of Monastir, Monastir, Tunisia

 PDF, 1901 kB

DOI: 10.18287/2412-6179-CO-605

Pages: 363-374.

Full text of article: English language.

Abstract:
Image processing is an effective method for characterizing various two-phase gas/liquid flow systems. However, bubbly flows at a high void fraction impose significant challenges such as diverse bubble shapes and sizes, large overlapping bubble clusters occurrence, as well as out-of-focus bubbles. This study describes an efficient multi-level image processing algorithm for highly overlapping bubbles recognition. The proposed approach performs mainly in three steps: overlapping bubbles classification, contour segmentation and arcs grouping for bubble reconstruction. In the first step, we classify bubbles in the image into a solitary bubble and overlapping bubbles. The purpose of the second step is overlapping bubbles segmentation. This step is performed in two subsequent steps: at first, we classify bubble clusters into touching and communicating bubbles. Then, the boundaries of communicating bubbles are split into segments based on concave point extraction. The last step in our algorithm addresses segments grouping to merge all contour segments that belong to the same bubble and circle/ellipse fitting to reconstruct the missing part of each bubble. An application of the proposed technique to computer generated and high-speed real air bubble images is used to assess our algorithm. The developed method provides an accurate and computationally effective way for overlapping bubbles segmentation. The accuracy rate of well segmented bubbles we achieved is greater than 90 % in all cases. Moreover, a computation time equal to 12 seconds for a typical image (1 Mpx, 150 overlapping bubbles) is reached.

Keywords:
bubble images; highly overlapping bubbles; bubble recognition; image segmentation; digital image processing.

Citation:
Bettaieb A, Filali N, Filali T, Ben Aissia H. An efficient algorithm for overlapping bubbles segmentation. Computer Optics 2020; 44(3): 363-374. DOI: 10.18287/2412-6179-CO-605.

References:

  1. Wang D, Song K, Fu Y, Liu Y. Integration of conductivity probe with optical and X-ray imaging systems for local air-water two-phase flow measurement. Meas Sci Technol 2018; 29(10): 105301.
  2. Wang D, Liu Y, Talley JD. Numerical evaluation of the uncertainty of double-sensor conductivity probe for bubbly flow measurement. Int J Multiph Flow 2018; 107: 51-66.
  3. Mercado JM, Gmez DC, Gils DV, Sun C, Lohse D. On bubble clustering and energy spectra in pseudo-turbulence. J Fluid Mech 2010; 650: 287-306.
  4. Ristic SS, Ilic JT, Cantrak DS, Ristic OR, Jankovic NZ. Estimation of laser-Doppler anemometry measuring volume displacement in cylindrical pipe flow. Thermal Science 2012; 16(4): 1027-1042.
  5. Song K, Liu Y. A compact X-ray system for two-phase flow measurement. Meas Sci Technol 2018; 29(2): 025305.
  6. Bieberle A, Härting H-U, Rabha S, Schubert M, Hampel U. Gamma-ray computed tomography for imaging of multiphase flows. Chemie Ingenieur Technik 2013; 85(7): 1002-1011. DOI: 10.1002/cite.201200250.
  7. Bouche E, Roig V, Risso F, Billet AM. Homogeneous swarm of high-Reynolds-number bubbles rising within a thin gap. Part 1. Bubble dynamics. J Fluid Mech 2012; 704: 211-231.
  8. Bouche E, Roig V, Risso F, Billet A-M. Homogeneous swarm of high-Reynolds-number bubbles rising within a thin gap. Part 2. Liquid dynamics. J Fluid Mech 2014; 758: 508-521.
  9. Fu Y, Liu Y. 3D bubble reconstruction using multiple cameras and space carving method. Meas Sci Technol 2018; 29(7): 075206.
  10. Cerqueira RFL, Paladino EE, Ynumaru BK, Maliska CR. Image processing techniques for the measurement of two-phase flow bubbly pipe flow using particle image and tracking velocimetry (PIV/PTV). Chem Eng Sci 2018; 189: 1-23.
  11. Yan X, Jia Y, Wang L, Cao Y. Drag coefficient fluctuation prediction of a single bubble rising in water. Chem Eng J 2017; 316: 553-562.
  12. Wang H, He X, Vishwanath P, Xiao-Zhi G. A Novel one-camera-five-mirror three-dimensional imaging method for reconstructing the cavitation bubble cluster in a water hydraulic valve haihang. Appl Sci 2018; 8(10): 1783.
  13. Zhao L, Sun L, Mo Z, Tang J, Hu L, Bao J. An investigation on bubble motion in liquid flowing through a rectangular Venturi channel. Experimental Thermal and Fluid Science 2018; 97: 48-58.
  14. Xue T, Xu LS, Zhang SZ. Bubble behavior characteristics based on virtual binocular stereo vision. Optoelectronics Letters 2018; 14(1): 44-47.
  15. Zhao L, Mo Z, Sun L, Xie G, Liu H, Du M, Tang J. A visualized study of the motion of individual bubbles in a venturi type bubble generator. Progress in Nuclear Energy 2017; 97: 74-89.
  16. Zhong S, Zou X, Zhang Z, Tian H. A flexible image analysis method for measuring bubble parameters, Chem Eng Sci 2016; 141: 143-153.
  17. Besbes S, El Hajem M, Ben Aissia H, Champagne JY. PIV measurements and Eulerian-Lagrangian simulations of the unsteady gas-liquid flow in a needle sparger rectangular Bubble column. Chem Eng Sci 2015; 126: 560-572.
  18. Mena PC, Pons MN, Teixeira JA, Rocha FA. Using image analysis in the study of multiphase gas absorption. Chem Eng Sci 2005; 60: 5144-5150.
  19. Bailey M, Gomez CO, Finch JA. Development and application of an image analysis method for wide bubble size distributions. Miner Eng 2005; 18: 1214-1221.
  20. Kracht W, Emery X, Paredes C. Astochastic approach for measuring bubble size distribution via image analysis. Int J Miner 2013; 121: 6-11.
  21. Honkanen M, Saarenrinne P, Stoor T, Niinimaki J. Recognition of highly overlapping ellipse-like bubble images. Meas Sci Technol 2005; 16: 1760-1770.
  22. Bleau A, Leon LJ. Watershed-based segmentation and region merging. Comput Vis Image Underst 2000; 77: 317-370.
  23. Teh CH, Chin RT. On the detection of dominant points on digital curves. IEEE Trans Pattern Anal Mach Intell 1989; 11: 859-872.
  24. Pei SC, Horng JH. Circular arc detection based on Hough transform. Patt Recogn Lett 1995; 16: 615-625.
  25. Mathai V, Prakash VN, Brons J, Sun C, Lohse D. Wake-driven dynamics of finite-sized buoyant spheres in turbulence. Phys Rev Lett 2015; 115: 124501.
  26. Prakash VN, Tagawa Y, Calzavarini E, Mercado JM, Toschi F, Lohse D, Sun C. How gravity and size affect the acceleration statistics of bubbles in turbulence. New J Phys 2012; 14: 105017.
  27. Honkanen M. Reconstruction of a three-dimensional bubble surface from high-speed orthogonal imaging of dilute bubbly flow. WIT Trans Eng Sci 2009; 63: 469-480.
  28. Lau YM, Deen NG, Kuipers JAM. Development of an image measurement technique for size distribution in dense bubbly flows. Chem Eng Sci 2013; 94: 20-29.
  29. Karn A, Ellis C, Arndt R, Hong J. An integrative image measurement technique for dense bubbly flows with a wide size distribution. Chem Eng Sci 2015; 122: 240-249.
  30. Villegas LR, Colombet D, Guiraud P, Legendre D, Cazinac S, Cockx A. Image processing for the experimental investigation of dense dispersed flows: application to bubbly flows. Int J Multiphase Flow 2019; 111: 16-30.
  31. Zhang WH, Jiang X, Liu YM. A method for recognizing overlapping elliptical bubbles in bubble image. Pattern Recogn Lett 2012; 33(12): 1543-1548.
  32. Fu Y, Liu Y. Development of a robust image processing technique for bubbly flow measurement in a narrow rectangular channel. Int J Multiphase Flow 2016; 84: 217-228.
  33. Langlard M, Al-Saddik H, Charton S, Debayle J. An efficiency improved recognition algorithm for highly overlapping ellipses: application to dense bubbly flows. Pattern Recogn Lett 2018; 101: 88-95.
  34. Liu X, Fang S. A convenient and robust edge detection method based on ant colony optimization. Opt Commun 2015; 353: 147-157.
  35. Bettaieb A, Filali N, Filali T, Ben Aissia H. GPU acceleration of edge detection algorithm based on local variance and integral image: application to air bubbles boundaries extraction. Computer Optics 2019; 43(3): 446-454. DOI: 10.18287/2412-6179-2019-43-3-446-454.
  36. Parvez MT, Mahmoud SA. Polygonal approximation of digital planar curves through adaptive optimizations. Pattern Recogn Lett 2010; 13: 1997-2005.
  37. Man D, Uda K, Ito Y, Nakano K. Accelerating computation of Euclidean distance map using the GPU with efficient memory access. Int J Paral Emergent Distrib Sys 2013; 28: 383-406.
  38. Zhang WH, Jiang X, Liu YM. A method for recognizing overlapping elliptical bubbles in bubble image. Pattern Recogn Lett 2012; 33: 1543-1548.
  39. Altheimer M, Häfeli R, Wälchli C, von Rohr PR. Shadow imaging in bubbly gas-liquid two-phase flow in porous structures. Exp Fluids 2015; 56(9): 177.
  40. Farhan M, Yli-Harja O, Niemistö A. A novel method for splitting clumps of convex object incorporating image intensity and using rectangular window-based concavity point pair search. Pattern Recogn 2013; 46: 741-751.
  41. Wencheng Wang, Xiaohui Yuan. Seperating touching particles: A concavity-based method using the area ratio of a circular mask. IEEE Systems, Man, and Cybernetics Magazine 2018; 4(2): 24-32.
  42. Akinlar C, Topal C. ED Circles: A real-time circle detector with a false detection control. Pattern Recogn 2013; 46: 725-740.
  43. Zafari S, Eerola T, Sampo J, Kälviäinen H, Haario H. Segmentation of partially overlapping nanoparticles using concave points, International Symposium on Visual Computing. ISVC 2015: Advances in Visual Computing 2015: 187-197.
  44. Park C, Huang JZ, Ji JX, Ding Y. Segmentation, inference and classification of partially overlapping nanoparticles. IEEE Trans Pattern Anal Mach Intell 2013; 35(3): 669-681.
  45. Langlard M. La géométrie aléatoire pour la caractérisation 3D de populations denses de particules : application aux écoulements diphasiques. PhD thesis. University of Lyon 2019.

© 2009, IPSI RAS
151, Molodogvardeiskaya str., Samara, 443001, Russia; E-mail: ko@smr.ru ; Tel: +7 (846) 242-41-24 (Executive secretary), +7 (846) 332-56-22 (Issuing editor), Fax: +7 (846) 332-56-20