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Machine Vision and Applications

, Volume 23, Issue 4, pp 675–689 | Cite as

Performance of optical flow techniques for motion analysis of fluorescent point signals in confocal microscopy

  • José Delpiano
  • Jorge Jara
  • Jan Scheer
  • Omar A. Ramírez
  • Javier Ruiz-del-Solar
  • Steffen HärtelEmail author
Special Issue Paper

Abstract

Optical flow approaches calculate vector fields which determine the apparent velocities of objects in time-varying image sequences. They have been analyzed extensively in computer science using both natural and synthetic video sequences. In life sciences, there is an increasing need to extract kinetic information from temporal image sequences which reveals the interplay between form and function of microscopic biological structures. In this work, we test different variational optical flow techniques to quantify the displacements of biological objects in 2D fluorescent image sequences. The accuracy of the vector fields is tested for defined displacements of fluorescent point sources in synthetic image series which mimic protein traffic in neuronal dendrites, and for GABABR1 receptor subunits in dendrites of hippocampal neurons. Our results reveal that optical flow fields predict the movement of fluorescent point sources within an error of 3% for a maximum displacement of 160 nm. Displacement of agglomerated GABABR1 receptor subunits can be predicted correctly for maximum displacements of 640 nm. Based on these results, we introduce a criteria to derive the optimum parameter combinations for the calculation of the optical flow fields in experimental images. From these results, temporal sampling frequencies for image acquisition can be derived to guarantee correct motion estimation for biological objects.

Keywords

Optical flow Motion estimation Light microscopy Fluorescence 

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138_2011_362_MOESM1_ESM.pdf (127 kb)
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References

  1. 1.
    Ramírez O.A., García A., Rojas R., Couve A., Härtel S.: Confined displacement algorithm for the discrimination of true and random colocalization of fluorescence signals in confined cellular compartments. J. Microsc. 239(3), 173–183 (2010)MathSciNetCrossRefGoogle Scholar
  2. 2.
    Ramírez O.A., Vidal R.L., Tello J.A., Vargas K.J., Kindler S., Hartel S., Couve A.: Dendritic assembly of heteromeric gamma-aminobutyric acid type B receptor subunits in hippocampal neurons. J. Biol. Chem. 284(19), 13077–13085 (2009)CrossRefGoogle Scholar
  3. 3.
    Härtel S., Jara J., Lemus C.G., Concha M.L.: 3D morpho-topological analysis of asymmetric neuronal morphogenesis in developing zebrafish. In: Tavares, J., Natal, J.R. (eds) Computational Modelling of Objects Represented in Images. Fundamentals, Methods and Applications, vol. 6, pp. 215–220. Taylor and Francis, New York (2007)Google Scholar
  4. 4.
    Gustafsson M.G., Shao L., Carlton P.M., Wang C.J., Golubovskaya I.N., Cande W.Z., Agard D.A., Sedat J.W.: Three-dimensional resolution doubling in wide-field fluorescence microscopy by structured illumination. Biophys. J. 94(12), 4957–4970 (2008)CrossRefGoogle Scholar
  5. 5.
    Hell S.W., Nagorni M.: 4Pi confocal microscopy with alternate interference. Opt. Lett. 23(20), 1567–1569 (1998)CrossRefGoogle Scholar
  6. 6.
    Betzig E., Patterson G.H., Sougrat R., Lindwasser O.W., Olenych S., Bonifacino J.S., Davidson M.W., Lippincott-Schwartz J., Hess H.F.: Imaging intracellular fluorescent proteins at nanometer resolution. Science 313(5793), 1642–1645 (2006)CrossRefGoogle Scholar
  7. 7.
    Hell S.W., Dyba M., Jakobs S.: Concepts for nanoscale resolution in fluorescence microscopy. Curr. Opin. Neurobiol. 14(5), 599–609 (2004)CrossRefGoogle Scholar
  8. 8.
    Keller P.J., Stelzer E.H.: Quantitative in vivo imaging of entire embryos with digital scanned laser light sheet fluorescence microscopy. Curr. Opin. Neurobiol. 18(6), 624–632 (2008)CrossRefGoogle Scholar
  9. 9.
    Aubert G., Kornprobst P.: Mathematical Problems in Image Processing. Partial Differential Equations and the Calculus of Variations, vol. 147. Applied Mathematical Sciences. Springer, Berlin (2006)Google Scholar
  10. 10.
    Fanani M.L., De Tullio L., Hartel S., Jara J., Maggio B.: Sphingomyelinase-induced domain shape relaxation driven by out-of-equilibrium changes of composition. Biophys. J. 96(1), 67–76 (2009)CrossRefGoogle Scholar
  11. 11.
    Fanani M.L., Härtel S., Maggio B., De Tullio L., Jara J., Olmos F., Oliveira R.G.: The action of sphingomyelinase in lipid monolayers as revealed by microscopic image analysis. Biochim. Biophys. Acta 1798(7), 1309–1323 (2010)CrossRefGoogle Scholar
  12. 12.
    Fidorra M., Heimburg T., Seeger H.M.: Melting of individual lipid components in binary lipid mixtures studied by FTIR spectroscopy, DSC and Monte Carlo simulations. Biochim. Biophys. Acta 1788(3), 600–607 (2009)CrossRefGoogle Scholar
  13. 13.
    Härtel S., Fanani M.L., Maggio B.: Shape transitions and lattice structuring of ceramide-enriched domains generated by sphingomyelinase in lipid monolayers. Biophys. J. 88(1), 287–304 (2005)CrossRefGoogle Scholar
  14. 14.
    Hubený, J., Ulman, V., Matula, P.: Estimating large local motion in live-cell imaging using variational optical flow, towards motion tracking in live cell imaging using optical flow. In: Proceedings of VISAPP 2007, Second International Conference on Computer Vision Theory and Applications, pp. 542–548 (2007)Google Scholar
  15. 15.
    Hamou A.K., El-Sakka M.R.: Optical flow active contours with primitive shape priors for echocardiography. EURASIP J. Adv. Signal Process 2010, 1–11 (2010)CrossRefGoogle Scholar
  16. 16.
    Baraldi P., Sarti A., Lamberti C., Prandini A., Sgallari F.: Evaluation of differential optical flow techniques on synthesized echo images. IEEE Trans. Biomed. Eng. 43(3), 259–272 (1996)CrossRefGoogle Scholar
  17. 17.
    Abramoff M.D., Viergever M.A.: Computation and visualization of three-dimensional soft tissue motion in the orbit. IEEE Trans. Med. Imaging 21(4), 296–304 (2002)CrossRefGoogle Scholar
  18. 18.
    Roberts T.J., McKenna S.J., Du C.J., Wuyts N., Valentine T.A., Bengough A.G.: Estimating the motion of plant root cells from in vivo confocal laser scanning microscopy images. Mach. Vis. Appl. 21, 921–939 (2010)CrossRefGoogle Scholar
  19. 19.
    Ben-Tekaya H., Miura K., Pepperkok R., Hauri H.P.: Live imaging of bidirectional traffic from the ERGIC. J. Cell Sci. 118(2), 357–367 (2005)CrossRefGoogle Scholar
  20. 20.
    Lombardot, B., Luengo-Oroz, M., Melani, C., Faure, E., Santos, A., Peyrieras, N., Ledesma-Carbayo, M., Bourgine, P.: Evaluation of four 3d non rigid registration methods applied to early zebrafish development sequences. MIAAB MICCAI (2008)Google Scholar
  21. 21.
    Gerencser A.A., Nicholls D.G.: Measurement of instantaneous velocity vectors of organelle transport: mitochondrial transport and bioenergetics in hippocampal neurons. Biophys. J. 95(6), 3079–3099 (2008)CrossRefGoogle Scholar
  22. 22.
    Buibas M., Yu D., Nizar K., Silva G.: Mapping the spatiotemporal dynamics of calcium signaling in cellular neural networks using optical flow. Ann. Biomed. Eng. 38(8), 2520–2531 (2010)CrossRefGoogle Scholar
  23. 23.
    Melani, C., Campana, M., Lombardot, B., Rizzi, B., Veronesi, F., Zanella, C., Bourgine, P., Mikula, K., Peyrieras, N., Sarti, A.: Cells tracking in a live zebrafish embryo. In: Conf Proc IEEE Eng Med Biol Soc 2007, pp. 1631–1634 (2007)Google Scholar
  24. 24.
    Horn B.K.P., Schunck B.G.: Determining optical flow. Artif. Intell. 17, 185–204 (1981)CrossRefGoogle Scholar
  25. 25.
    Bruhn A., Weickert J., Feddern C., Kohlberger T., Schnorr C.: Variational optical flow computation in real time. IEEE Trans. Image Process. 14(5), 608–615 (2005)MathSciNetCrossRefGoogle Scholar
  26. 26.
    Papenberg N., Bruhn A., Brox T., Didas S., Weickert J.: Highly accurate optic flow computation with theoretically justified warping. Int. J. Comput. Vis. 67(2), 141–158 (2006)CrossRefGoogle Scholar
  27. 27.
    Bruhn A., Weickert J., Schnörr C.: Combining the advantages of local and global optic flow methods. In: Gool, L. (eds) Proc. 24th DAGM Symposium on Pattern Recognition, vol. 2449, pp. 454–462. Springer, Zurich (2002)Google Scholar
  28. 28.
    Bruhn A., Weickert J., Schnörr C.: Lucas/Kanade meets Horn/Schunck: combining local and global optic flow methods. Int. J. Comput. Vis. 61(3), 211–231 (2005)CrossRefGoogle Scholar
  29. 29.
    Bruhn A., Weickert J., Feddern C., Kohlberger T., Schnorr C.: Real-time optic flow computation with variational methods. Proc. Comput. Anal. Images Patterns 2756, 222–229 (2003)MathSciNetCrossRefGoogle Scholar
  30. 30.
    Lucas, B.D., Kanade, T.: An iterative image registration technique with an application to stereo vision. In: International Joint Conference on Artificial Intelligence, vol. 3, pp. 674–679 (1981)Google Scholar
  31. 31.
    Sorensen, T.S., Noe, K.O., Christoffersen, C.P.V., Kristiansen, M., Mouridsen, K., Osterby, O., Brix, L.: Active contours in optical flow fields for image sequence segmentation. In: Biomedical Imaging: From Nano to Macro, 2010 IEEE International Symposium on. pp. 916–919 (2010)Google Scholar
  32. 32.
    Cornelius, N., Kanade, T.: Adapting optical-flow to measure object motion in reflectance and X-ray image sequences. In: Proceedings of the ACM SIGGRAPH/SIGART Interdisciplinary Workshop on Motion, pp. 145–153 (1986)Google Scholar
  33. 33.
    Castro J., Ruminot I., Porras O.H., Flores C.M., Hermosilla T., Verdugo E., Venegas F., Härtel S., Michea L., Barros L.F.: ATP steal between cation pumps: a mechanism linking Na+ influx to the onset of necrotic Ca2+ overload. Cell Death Different. 13, 1675–1685 (2006)CrossRefGoogle Scholar
  34. 34.
    Härtel S., Zorn-Kruppa M., Tykhonova S., Alajuuma P., Engelke M., Diehl H.: Staurosporine-induced apoptosis in human cornea epithelial cells in vitro. Cytometry 08, 15–23 (2003)CrossRefGoogle Scholar
  35. 35.
    Sanchez G., Escobar M., Pedrozo Z., Macho P., Domenech R., Hartel S., Hidalgo C., Donoso P.: Exercise and tachycardia increase NADPH oxidase and ryanodine receptor-2 activity: possible role in cardioprotection. Cardiovasc. Res. 77(2), 380–386 (2008)CrossRefGoogle Scholar
  36. 36.
    Espinosa A., Garcia A., Hartel S., Hidalgo C., Jaimovich E.: NADPH oxidase and hydrogen peroxide mediate insulin-induced calcium increase in skeletal muscle cells. J. Biol. Chem. 284(4), 2568–2575 (2009)CrossRefGoogle Scholar
  37. 37.
    Parra V., Eisner V., Chiong M., Criollo A., Moraga F., Garcia A., Hartel S., Jaimovich E., Zorzano A., Hidalgo C., Lavandero S.: Changes in mitochondrial dynamics during ceramide-induced cardiomyocyte early apoptosis. Cardiovasc. Res. 77(2), 387–397 (2008)CrossRefGoogle Scholar
  38. 38.
    Box G.E.P., Muller M.E.: note on the generation of random normal deviates. Ann. Math. Stat. 29, 610–611 (1958)zbMATHCrossRefGoogle Scholar
  39. 39.
    Barron J.L., Fleet D.J., Beauchemin S.S.: Performance of optical-flow techniques. Int. J. Comput. Vis. 12(1), 43–77 (1994)CrossRefGoogle Scholar
  40. 40.
    Costes S.V., Daelemans D., Cho E.H., Dobbin Z., Pavlakis G., Lockett S.: Automatic and quantitative measurement of protein-protein colocalization in live cells. Biophys. J. 86, 3993–4003 (2004)CrossRefGoogle Scholar
  41. 41.
    Comeau J.W., Kolin D.L., Wiseman P.W.: Accurate measurements of protein interactions in cells via improved spatial image cross-correlation spectroscopy. Mol. Biosyst. 4, 672–685 (2008)CrossRefGoogle Scholar
  42. 42.
    Ruhnau P., Kohlberger T., Schnörr C., Nobach H.: Variational optical flow estimation for particle image velocimetry. Exp. Fluids 38(1), 21–32 (2005)CrossRefGoogle Scholar
  43. 43.
    Demandolx D., Davoust J.: Multicolour analysis and local image correlation in confocal microscopy. J. Microsc. 185, 21–36 (1997)CrossRefGoogle Scholar
  44. 44.
    Lachmanovich E., Shvartsman D.E., Malka Y., Botvin C., Henis Y.I., Weiss A.M.: Co-localization analysis of complex formation among membrane proteins by computerized fluorescence microscopy: application to immunofluorescence co-patching studies. J. Microsc. 212, 122–131 (2003)MathSciNetCrossRefGoogle Scholar
  45. 45.
    van Kempen G.: Image Restoration in Fluorescence Microscopy. Technische Universiteit Delft, Netherlands (1999)Google Scholar
  46. 46.
    Bruhn A., Weickert J., Kohlberger T., Schnorr C.: A multigrid platform for real-time motion computation with discontinuity-preserving variational methods. Int. J. Comput. Vis. 70(3), 257–277 (2006)CrossRefGoogle Scholar
  47. 47.
    Hirokawa N., Noda Y.: Intracellular transport and kinesin superfamily proteins, KIFs: structure, function, and dynamics. Physiol. Rev. 88(3), 1089–1118 (2008)CrossRefGoogle Scholar
  48. 48.
    Racine V., Sachse M., Salamero J., Fraisier V., Trubuil A., Sibarita J.B.: Visualization and quantification of vesicle trafficking on a three-dimensional cytoskeleton network in living cells. J. Microsc. 225(Pt 3), 214–228 (2007)MathSciNetCrossRefGoogle Scholar
  49. 49.
    Broeke J.H., Ge H., Dijkstra I.M., Cemgil A.T., Riedl J.A., Cornelisse L.N., Toonen R.F., Verhage M., Fitzgerald W.J.: Automated quantification of cellular traffic in living cells. J. Neurosci. Methods 178(2), 378–384 (2009)CrossRefGoogle Scholar
  50. 50.
    Misko A., Jiang S., Wegorzewska I., Milbrandt J., Baloh R.H.: Mitofusin 2 is necessary for transport of axonal mitochondria and interacts with the Miro/Milton complex. J. Neurosci. 30(12), 4232–4240 (2010)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag 2011

Authors and Affiliations

  • José Delpiano
    • 1
    • 2
  • Jorge Jara
    • 3
    • 4
  • Jan Scheer
    • 3
  • Omar A. Ramírez
    • 3
  • Javier Ruiz-del-Solar
    • 1
  • Steffen Härtel
    • 3
    Email author
  1. 1.Department of Electrical Engineering, Faculty of Physical and Mathematical SciencesUniversity of ChileSantiagoChile
  2. 2.Faculty of Engineering and Applied SciencesUniversity of the AndesSantiagoChile
  3. 3.Laboratory for Scientific Image Analysis (SCIAN-Lab) at the Program of Anatomy and Developmental Biology and the Biomedical Neuroscience Institute BNI, ICBM, Faculty of MedicineUniversity of ChileSantiagoChile
  4. 4.Department of Computer Sciences, Faculty of Physical and Mathematical SciencesUniversity of ChileSantiagoChile

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