Hostname: page-component-745bb68f8f-5r2nc Total loading time: 0 Render date: 2025-01-22T09:00:21.618Z Has data issue: false hasContentIssue false
  • English
  • Français

Estimation of plant cell wall thickness and cell size by image skeletonization

Published online by Cambridge University Press:  27 March 2009

A. J. Travis ,
S. D. Murison  and
A. Chesson
A. J. Travis
Affiliation:
Rowett Research Institute, Greenburn Road, Bucksburn, Aberdeen AB2 9SB, UK
S. D. Murison
Affiliation:
Rowett Research Institute, Greenburn Road, Bucksburn, Aberdeen AB2 9SB, UK
A. Chesson
Affiliation:
Rowett Research Institute, Greenburn Road, Bucksburn, Aberdeen AB2 9SB, UK
Get access Rights & Permissions [Opens in a new window]

Summary

A system for automatically measuring the mean cell-wall thickness in a user-defined area of plant tissue has been developed using image analysis. The digitized grey-level image of a tissue section is first segmented using a histogram-partitioning algorithm. The resulting binary image is then repeatedly thinned until the minimum connected set of pixels, or ‘skeleton’, remains. A nearestneighbour length estimator is used to calculate the total length of the skeleton which approximates to the location of the middle lamella in the original section. The length of the skeleton and the number of nodes it contains are used to estimate the mean cell radius, and mean cell-wall thickness using the area of cell-wall material in the segmented binary image. The method has been used to estimate mean cell-wall thickness along a newly extended Zea mays internode, and the results are compared to measurements obtained manually using a micrometer ‘line’. The techniques of rapidly assessing mean cell-wall thickness and cell dimensions using image analysis are needed to assess how much of the variation in nutritive value between forage cultivars can be ascribed to changes in cell-wall chemistry and how much to anatomical differences.

Type
Crops and Soils
Information
The Journal of Agricultural Science , Volume 120 , Issue 3 , June 1993 , pp. 279 - 287
Copyright
Copyright © Cambridge University Press 1993

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

REFERENCES

Dorst, L. & Smeulders, A. W. M. (1987). Length estimators for digitised contours. Computer Vision, Graphics and Image Processing 40, 311–333.CrossRefGoogle Scholar
Dunn, G. J. & Briggs, K. G. (1989). Variation in culm anatomy among barley cultivars differing in lodging resistance. Canadian Journal of Botany 67, 1838–1843.CrossRefGoogle Scholar
Freeman, H. (1970). Boundary coding and processing. In Picture Processing and Psychopictorics (Eds Lipkin, B. S. & Rosenfeld, A.), pp. 241–266. New York: Academic Press.Google Scholar
Hilditch, C. J. (1969). Linear skeletons from square cupboards. Machine Intelligence 4, 403–420.Google Scholar
Kisser, J. & Halbwachs, G. (1972). Permanent staining of plant cell walls in glycerol and also in Canada balsam for quantitative image analysis. Mikroskopie 28, 225–247.Google Scholar
Riddler, T. W. & Calvard, S. (1978). Picture thresholding using an iterative selection method. IEEE Transactions on Systems, Man and Cybernetics SMC-8, 630–632.Google Scholar
Schertz, K. F. & Rosenow, D. T. (1977). Anatomical variation in stalk internodes of sorghum. Crop Science 17, 628–631.CrossRefGoogle Scholar
Tarbell, K. A. & Reid, J. F. (1991). A computer vision system for characterising corn growth and development. Transactions of the American Society of Agricultural Engineers 34, 2245–2255.CrossRefGoogle Scholar
Trussel, H. J. (1979). Comments on ‘picture thresholding using an iterative selection method’. IEEE Transactions on Systems, Man, and Cybernetics 9, 311.CrossRefGoogle Scholar
Twidwell, E. K., Johnson, K. D., Bracker, C. E., Patterson, J. A. & Cherney, J. H. (1989). Plant tissue degradation measurement using image analysis. Agronomy Journal 81, 837–840.CrossRefGoogle Scholar
Van Vuuren, A. M., Tamminga, S. & Ketelaar, R. S. (1991). In sacco degradation of organic matter and crude protein of fresh grass (Lolium perenne) in the rumen of grazing dairy cows. Journal of Agricultural Science, Cambridge 116, 429–436.CrossRefGoogle Scholar
White, E. M. (1991). Response of winter barley cultivars to nitrogen and a plant growth regulator in relation to lodging. Journal of Agricultural Science, Cambridge 116, 191–200.CrossRefGoogle Scholar
Wilson, J. R., Deinum, B. & Engels, F. M. (1991). Temperature effects on anatomy and digestibility of leaf and stem of tropical and temperate forage species. Netherlands Journal of Agricultural Science 39, 31–48.CrossRefGoogle Scholar
Young, I. T. (1989). Modern digital image analysis. Proceedings of International Conference on Acoustics, Speech and Signal Processing, Glasgow, IEEE publication S9CH2673–2, 1699–1702.CrossRefGoogle Scholar
Young, I. T., Peverini, R. L., Verbeek, P. W. & van Otterloo, P. J. (1981). A new implementation for the binary and Minkowski operators. Computer Graphics and Image Processing 17, 189–210.CrossRefGoogle Scholar