Machine Vision and Applications

, Volume 21, Issue 6, pp 921–939 | Cite as

Estimating the motion of plant root cells from in vivo confocal laser scanning microscopy images

  • Timothy J. Roberts
  • Stephen J. McKenna
  • Cheng-Jin Du
  • Nathalie Wuyts
  • Tracy A. Valentine
  • A. Glyn Bengough
Original Paper

Abstract

Images of cellular structures in growing plant roots acquired using confocal laser scanning microscopy have some unusual properties that make motion estimation challenging. These include multiple motions, non-Gaussian noise and large regions with little spatial structure. In this paper, a method for motion estimation is described that uses a robust multi-frame likelihood model and a technique for estimating uncertainty. An efficient region-based matching approach was used followed by a forward projection method. Over small timescales the dynamics are simple (approximately locally constant) and the change in appearance small. Therefore, a constant local velocity model is used and the MAP estimate of the joint probability over a set of frames is recovered. Occurrences of multiple modes in the posterior are detected, and in the case of a single dominant mode, motion is inferred using Laplace’e method. The method was applied to several Arabidopsis thaliana root growth sequences with varying levels of success. In addition, comparative results are given for three alternative motion estimation approaches, the Kanade–Lucas–Tomasi tracker, Black and Anandan’s robust smoothing method, and Markov random field based methods.

Keywords

Motion estimation Plant root Living cell Confocal laser scanning microscopy Uncertainty 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Baker S., Matthews I.: Lucas–Kanade 20 years on: a unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004)CrossRefGoogle Scholar
  2. 2.
    Barlow P.W.: Anatomical controls of root growth. Aspects Appl. Biol. 22, 57–66 (1989)MathSciNetGoogle Scholar
  3. 3.
    Barron J.L., Liptay L.: Measuring 3D plant growth using optical flow. BioImaging 5, 82–86 (1997)CrossRefGoogle Scholar
  4. 4.
    Bengough A.G., Bransby M.F., Hans J., McKenna S.J., Roberts T.J., Valentine T.A.: Root responses to soil physical conditions: growth dynamics from field to cell. J. Exp. Bot. Plast. Special Issue 57(2), 437–447 (2006)Google Scholar
  5. 5.
    Black M.J., Anandan P.: The robust estimation of multiple motions: parametric and piecewise-smooth flow-fields. Comput. Vis. Image Underst. 63(1), 75–104 (1996)CrossRefGoogle Scholar
  6. 6.
    Boykov, Y., Veksler, O., Zabih, R.: Fast approximate energy minimization via graph cuts. In: IEEE International Conference on Computer Vision, pp. 377–384 (1999)Google Scholar
  7. 7.
    Carreira-Perpiñán, M.A., Hinton, G.E.: On contrastive divergence learning. In: Workshop on Artificial Intelligence and Statistics, pp. 33–40 (2005)Google Scholar
  8. 8.
    Chavarria-Krauser A., Schurr U.: A cellular growth model for root tips. J. Theor. Biol. 230, 21–32 (2004)CrossRefMathSciNetGoogle Scholar
  9. 9.
    Felzenszwalb P.F., Huttenlocher D.P.: Efficient belief propagation for early vision. Int. J. Comput. Vis. 70(1), 261–268 (2006)CrossRefGoogle Scholar
  10. 10.
    Genovesio A., Liedl T., Emiliani V., Parak W.J., Coppey-Moisan M., Olivo-Marin J.C.: Multiple particle tracking in 3-d+t microscopy: method and application to the tracking of endocytosed quantum dots. IEEE Trans. Image Process. 15(5), 1062–1070 (2006)CrossRefGoogle Scholar
  11. 11.
    Gilroy S.: Fluorescence microscopy of living plant cells. Ann. Rev. Plant Physiol. Plant Mol. Biol. 48, 165–190 (1997)CrossRefGoogle Scholar
  12. 12.
    Hanson M.R., Köhler R.H.: GFP imaging: methodology and application to investigate cellular compartmentation in plants. J. Exp. Bot. 52(356), 529–539 (2001)CrossRefGoogle Scholar
  13. 13.
    Harauz G., Ottensmeyer F.P.: Interpolation in computing forward projections in direct three-dimensional reconstruction. Phys. Med. Biol. 28(12), 1419–1427 (1983)CrossRefGoogle Scholar
  14. 14.
    Horn B.K.P., Schunk B.G.: Determining optical flow. Artif. Intell. 17, 185–203 (1981)CrossRefGoogle Scholar
  15. 15.
  16. 16.
    Kohli, P., Torr, P.H.S.: Efficiently solving dynamic Markov random fields using graph cuts. In: IEEE International Conference on Computer Vision, vol. II, pp. 922–929 (2005)Google Scholar
  17. 17.
    Kolmogorov, V., Zabih, R. (2002) What energy functions can be minimized via graph cuts? In: European Conference on Computer Vision, pp. 65–81 (2002)Google Scholar
  18. 18.
    Kurup S., Runions J., Köhler U., Laplaze L., Hodge S., Haseloff J.: Marking cell lineages in living tissues. Plant 42(3), 444–453 (2005)CrossRefGoogle Scholar
  19. 19.
    Lam C.-Y.: Applied Numerical Methods for Partial Differential Equations. Prentice-Hall, New Jersey (1994)Google Scholar
  20. 20.
    Lan, X., Roth, S., Huttenlocher, D., Black, M.J.: Efficient belief propagation with learned higher-order markov random fields. In: European Conference on Computer Vision (2006)Google Scholar
  21. 21.
    Li S.: Markov Random Field Modeling in Computer Vision. Springer, Berlin (1995)Google Scholar
  22. 22.
    Likar B., Maintz J.B., Viergever M.A., Pernus F.: Retrospective shading correction based on entropy minimization. J. Microsc. 197(3), 285–295 (2000)CrossRefGoogle Scholar
  23. 23.
    Lucas, B.D., Kanade, T.: An iterative image registration technique with an application to stereo vision. In: International Joint Conference on Artificial Intelligence, pp. 674–679 (1981)Google Scholar
  24. 24.
    Mackay D.J.C.: Information Theory, Inference, and Learning Algorithms. Cambridge University Press, Cambridge (2002)Google Scholar
  25. 25.
    de F. Maraschin S., Vennik M., Lamers G.E.M., Spaink H.P., Wang M.: Time-lapse tracking of barley androgenesis reveals position-determined cell death within pro-embryos. Planta 220, 531–540 (2005)CrossRefGoogle Scholar
  26. 26.
    Model M.A., Burkhardt J.K.: A standard for calibration and shading correction of a fluorescence microscope. Cytometry 44(4), 309–316 (2001)CrossRefGoogle Scholar
  27. 27.
    Roberts, T.J., McKenna, S.J., Hans, J., Valentine, T.A., Bengough A.G.: Part-based multi-frame registration for estimation of the growth of cellular networks in plant roots. In: International Conference on Pattern Recognition, August (2006)Google Scholar
  28. 28.
    Scheres B., Benfey P., Dolan L.: Root development. In: Somerville, C.R., Meyerowitz, E.M. The Arabidopsis Book, American Society of Plant Biologists, Rockville (2002). doi:10.1199/tab.0101
  29. 29.
    Sharp R.E., Silk W.K., Hsiao T.C.: Growth of the primary maize root at low water potentials. I. Spatial distribution of expansive growth. Plant Physiol. 87, 50–57 (1988)CrossRefGoogle Scholar
  30. 30.
    Shimizu, M., Okutomi, M.: Precise sub-pixel estimation on area-based matching. In: IEEE International Conference on Computer Vision, pp. 90–97 (2001)Google Scholar
  31. 31.
    Singh A.: Optic Flow Computation: A Unified Perspective. IEEE Computer Society Press, California (1991)Google Scholar
  32. 32.
    Somleva M.N., Schmidt E.D.L., de Vries S.C.: Embryogenic cells in Dactylis glomerata L. (Poaceae) explants identified by cell tracking and by serk expression. Plant Cell Rep. 19, 718–726 (2002)CrossRefGoogle Scholar
  33. 33.
    Swarup R., Kramer E., Perry P., Knox K., Ottoline Leyser H.M., Haseloff J., Beemster G.T.S., Bhalerao R., Bennett M.J.: Root gravitropism requires lateral root cap and epidermal cells for transport and response to a mobile auxin signal. Nat. Cell Biol. 7, 1057–1065 (2005)CrossRefGoogle Scholar
  34. 34.
    Szeliski, R., Zabih, R., Scharstein, D., Veksler, O., Kolmogorov, V., Agarwala, A., Tappen, M., Rother, C.: A comparative study of energy minimization methods for markov random fields. In: European Conference on Computer Vision, Graz, Austria, May 2006, vol. 2, pp. 19–26 (2006)Google Scholar
  35. 35.
    Tappen, M., Freeman, W.: Comparison of graph cuts with belief propagation for stereo using identical MRF parameters. In: IEEE International Conference on Computer Vision, October 2003, vol. 2, pp. 900–908 (2003)Google Scholar
  36. 36.
    Toonen M.A.J., Hendriks T., Schmidt E.D.L., Verhoeven H.A., van Kammen A., de Vries S.C.: Description of somatic-embryoforming single cells in carrot suspension cultures employing video cell tracking. Planta 194, 565–572 (1994)CrossRefGoogle Scholar
  37. 37.
    MetaMorph Imaging System. Universal Imaging Corporation. http://www.image1.com/
  38. 38.
    van der Weele C.M., Jiang H.S., Palaniappan K.K., Ivanov V.B., Palaniappan K., Baskin T.I.: A new algorithm for computational image analysis of deformable motion at high spatial and temporal resolution applied to root growth. Plant Physiol. 132(3), 1138–1148 (2003)CrossRefGoogle Scholar
  39. 39.
    Walter A., Spies H., Terjung S., Küsters R., Kirchgessner N., Schurr U.: Spatio-temporal dynamics of expansion growth in roots: automatic quantification of diurnal course and temperature response by digital image sequence processing. J. Exp. Bot. 53(369), 689–698 (2002)CrossRefGoogle Scholar
  40. 40.
    Wyatt P.P., Noble J.A.: MAP MRF joint segmentation and registration of medical images. Med. Image Anal. 7(4), 539–552 (2003)CrossRefGoogle Scholar
  41. 41.
    Zeng G., Birchfield S., Wells C.E.: Detecting and measuring fine roots in minirhizotron images using matched filtering and local entropy thresholding. Mach. Vis. Appl. 17(4), 265–278 (2006)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag 2009

Authors and Affiliations

  • Timothy J. Roberts
    • 1
  • Stephen J. McKenna
    • 1
  • Cheng-Jin Du
    • 1
  • Nathalie Wuyts
    • 2
  • Tracy A. Valentine
    • 2
  • A. Glyn Bengough
    • 2
  1. 1.School of ComputingUniversity of DundeeDundeeScotland, UK
  2. 2.Scottish Crop Research InstituteDundeeScotland, UK

Personalised recommendations