Tracking Red Blood Cells Flowing through a Microchannel with a Hyperbolic Contraction: An Automatic Method

  • B. TaboadaEmail author
  • F. C. Monteiro
  • R. Lima
Part of the Lecture Notes in Computational Vision and Biomechanics book series (LNCVB, volume 19)


The present chapter aims to assess the motion and deformation index of red blood cells (RBCs) flowing through a microchannel with a hyperbolic contraction using an image analysis based method. For this purpose, a microchannel containing a hyperbolic contraction was fabricated in polydimethylsiloxane by using a soft-lithography technique and the images were captured by a standard high-speed microscopy system. An automatic image processing and analyzing method has been developed in a MATLAB environment, not only to track both healthy and exposed RBCs motion but also to measure the deformation index along the microchannel. The keyhole model has proved to be a promising technique to track automatically healthy and exposed RBCs flowing in this kind of microchannels.


Optical Flow Bilateral Filter Atomic Region Motion Segmentation Deformation Index 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



The authors acknowledge the financial support provided by PTDC/SAUBEB/105650/2008, PTDC/SAU-ENB/116929/2010, EXPL/EMS-SIS/2215/2013 from FCT (Science and Technology Foundation), COMPETE, QREN and European Union (FEDER).


  1. 1.
    Abkarian M, Faivre M, Horton R, Smistrup K, Best-Popescu CA, Stone HA (2008) Cellularscale hydrodynamics. Biomed Mater 3(3):034011CrossRefGoogle Scholar
  2. 2.
    Brox T, Bruhn A, Papenberg N, Weickert J (2004) High accuracy optical flow estimation based on a theory for warping. In: PajdlaT, Matas J (eds) European conference on computer vision, vol. 3024. Springer, LNCS, pp 25–36Google Scholar
  3. 3.
    Bruhn A, Weickert J, Schnörr C (2005) Luca/Kanade meets Horn/Schunck: combining local and global optic flow methods. Int J Comput Vision 61(3):1–21CrossRefGoogle Scholar
  4. 4.
    Carter BC, Shubeita GT, Gross SP (2005) Tracking single particles: a user-friendly quantitative evaluation. Phys Biol 2:60–72CrossRefGoogle Scholar
  5. 5.
    Crocker JC, Grier DG (1996) Methods of digital video microscopy for colloidal studies. J Colloid Interface Sci 179(1):298–310CrossRefGoogle Scholar
  6. 6.
    Faustino V, Pinho D, Yaginuma T, Calhelha R, Ferreira I, Lima R (2014) Ex-tensional flow-based microfluidic device: deformability assessment of red blood cells in contact with tumor cells. BioChip J 8:42–47CrossRefGoogle Scholar
  7. 7.
    Fujiwara H, Ishikawa T et al (2009) Red blood cell motions in high-hematocrit blood flowing through a stenosed microchannel. J Biomech 42:838–843CrossRefGoogle Scholar
  8. 8.
    Garcia V, Dias R, Lima R (2012) In vitro blood flow behaviour in microchannels with simple and complex geometries. In: Naik GR (ed) Applied biological engineering–principles and practice. InTech, Rijeka, pp 393–416Google Scholar
  9. 9.
    Horn BKP, Schunck BG (1981) Determining optical flow. Artif Intell 17(1–3):185–203CrossRefGoogle Scholar
  10. 10.
    Leble V, Lima R, Dias R, Fernandes C, Ishikawa T, Imai Y, Yamaguchi T (2011) Asymmetry of red blood cell motions in a microchannel with a diverging and converging bifurcation. Biomicrofluidics 5:044120CrossRefGoogle Scholar
  11. 11.
    LimaR (2007) Analysis of the blood flow behavior through microchannels by a confocal micro-PIV/PTV system. PhD (Eng), Bioengineering and Robotics Department, Tohoku University, Sendai, JapanGoogle Scholar
  12. 12.
    Lima R, Ishikawa T et al (2009) Measurement of individual red blood cell motions under high hematocrit conditions using a confocal micro-PTV system. Ann Biomed Eng 37:1546–1559CrossRefGoogle Scholar
  13. 13.
    Lima R, Ishikawa T, Imai Y, Takeda M, Wada S, Yamaguchi T (2008) Radial dispersion of red blood cells in blood flowing through glass capillaries: role of heamatocrit and geometry. J Biomech 44:2188–2196CrossRefGoogle Scholar
  14. 14.
    Lima R, Oliveira MSN, Ishikawa T, Kaji H, Tanaka S, Nishizawa, M, Yamaguchi T (2009) Axisymmetric PDMS microchannels for in vitro haemodynamics studies. Biofabrication 1(3):035005CrossRefGoogle Scholar
  15. 15.
    Lima R, Ishikawa T, Imai Y, Yamaguchi T (2012) Blood flow behavior in microchannels: advances and future trends. In: Dias R et al (eds) Single and two-phase flows on chemical and biomedical engineering. Bentham Science, Sharjah, pp 513–547Google Scholar
  16. 16.
    Lima R, Ishikawa T, Imai Y, Yamaguchi T (2013) Confocal micro-PIV/PTV measurements of the blood flow in micro-channels. In: Collins MW, König CS (eds) Nano and micro flow systems for bioanalysis, vol. 2. Springer, New York, pp 131–151Google Scholar
  17. 17.
    Lucas BD, Kanade T (1981) An iterative image registration technique with an application to stereo vision. Proceedings of Imaging Understanding Workshop, pp 121–130Google Scholar
  18. 18.
    Meijering E, Dzyubachyk O, Smal I (2012) Methods for cell and particle tracking. In: Conn PM (ed) Imaging and spectroscopic analysis of living cells. Methods in enzymology, vol. 504. Elsevier, Amsterdam, pp 183–200Google Scholar
  19. 19.
    Pinho D, Yaginuma T, Lima R (2013) A microfluidic device for partial cell separation and deformability assessment. BioChip J 7:367–374CrossRefGoogle Scholar
  20. 20.
    Pinho D, Gayubo F, Pereira AI, Lima R (2013) A comparison between a manual and automatic method to characterize red blood cell trajectories. Int J Numer Meth Biomed Eng 29(9):977–987CrossRefMathSciNetGoogle Scholar
  21. 21.
    Reyes-Aldasoro CC, Akerman S, Tozer G (2008) Measuring the velocity of fluorescently labelled red blood cells with a keyhole tracking algorithm. J Microsc 229(1):162–173CrossRefMathSciNetGoogle Scholar
  22. 22.
    Rodrigues R, Faustino V, Pinto E, Pinho D, Lima R (2014) Red blood cells deformability index assessment in a hyperbolic microchannel: the diamide and glutaraldehyde effect. WebmedCentralplus Biomedical Engineering. 1: WMCPLS00253Google Scholar
  23. 23.
    Sbalzarini IF, Koumoutsakos P (2005) Feature point tracking and trajectory analysis for video imaging in cell biology. J Struct Bio 151(2):182–195CrossRefGoogle Scholar
  24. 24.
    Smith MB, Karatekin E, Gohlke A, Mizuno H, Watanabe N, Vavylonis D (2011) Interactive, computer-assisted tracking of speckle trajectories in fluorescence microscopy: application to actin polymerization and membrane fusion. Biophys J 101:1794–1804CrossRefGoogle Scholar
  25. 25.
    Tomasi C, Manduchi R (1998) Bilateral filtering for gray and color images. International Conference on Computer Vision, pp 839–846Google Scholar
  26. 26.
    Vincent L, Soille P (1991) Watersheds in digital spaces: an efficient algorithm based on immersion simulations. IEEE PAMI 13(6):583–598CrossRefGoogle Scholar
  27. 27.
    Weiss Y (1997) Smoothness in layers: motion segmentation using nonparametric mixture estimation. Watersheds in digital spaces: An efficient algorithm based on immersion simulations, Int Conf on Computer Vision and Pattern Recognition, pp 520–527Google Scholar
  28. 28.
    Yaginuma T, Oliveira MS, Lima R, Ishikawa T, Yamaguchi T (2013) Human red blood cell behavior under homogeneous extensional flow in a hyperbolic-shaped microchannel. Biomicrofluidics 7:54110CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  1. 1.ESTiG, IPB, C. Sta. ApoloniaBragançaPortugal
  2. 2.CEFT, FEUP, R. Dr. Roberto FriasPortoPortugal
  3. 3.University of MinhoMechanical Engineering DepartmentGuimarãesPortugal

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