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Machine Vision and Applications

, Volume 21, Issue 3, pp 309–317 | Cite as

Rapid automated detection of roots in minirhizotron images

  • Guang Zeng
  • Stanley T. Birchfield
  • Christina E. Wells
Original Paper

Abstract

An approach for rapid, automatic detection of plant roots in minirhizotron images is presented. The problem is modeled as a Gibbs point process with a modified Candy model, in which the energy functional is minimized using a greedy algorithm whose parameters are determined in a data-driven manner. The speed of the algorithm is due in part to the selection of seed points, which discards more than 90% of the data from consideration in the first step. Root segments are formed by grouping seed points into piecewise linear structures, which are further combined and validated using geometric techniques. After root centerlines are found, root regions are detected using a recursively bottom-up region growing method. Experimental results from a collection of diverse root images demonstrate improved accuracy and faster performance compared with previous approaches.

Keywords

Line Segment Seed Point Root Region Retinal Fundus Image Image Intensity Function 
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.

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References

  1. 1.
    Andren O., Elmquist H., Hansson A.C.: Recording processing and analysis of grass root images from a rhizotron. Plant Soil 185(2), 259–264 (1996)CrossRefGoogle Scholar
  2. 2.
    Birchfield, S., Wells, C.E.: Rootfly: Software for Minirhizotron Image Analysis. http://www.ces.clemson.edu/~stb/rootfly/
  3. 3.
    Can A., Shen H., Turner J.N., Tanenbaum H.L., Roysam B.: Rapid automated tracing and feature extraction from retinal fundus images using direct exploratory algorithms. IEEE Trans. Inform. Technol. Biomed. 3(2), 125–138 (1999)CrossRefGoogle Scholar
  4. 4.
    Erz, G., Posch, S.: Root detection by hierarchical seed expansion. In: Proceedings of the International Conference on Computer as a Tool (EROCON 2005), pp. 963–966 (2005)Google Scholar
  5. 5.
    Kimura K., Kikuchi S., Yamasaki S.: Accurate root length measurement by image analysis. Plant Soil 216(1), 117–127 (1999)CrossRefGoogle Scholar
  6. 6.
    Lebowitz R.J.: Digital image analysis measurement of root length and diameter. Environ. Exp. Bot. 28(3), 267–273 (1988)CrossRefGoogle Scholar
  7. 7.
    Nater E.A., Nater K.D., Baker J.M.: Application of artificial neural system algorithms to image analysis of roots in soil. Geoderma 53(3), 237–253 (1992)CrossRefGoogle Scholar
  8. 8.
    Phillips D.L., Johnson M.G., Tingey D.T., Biggart C., Nowak R.S., Newsom J.C.: Minirhizotron installation in sandy, rocky soils with minimal soil disturbance. Soil Sci. Soc. Am. J. 61, 761–764 (2000)Google Scholar
  9. 9.
    Poli R., Valli G.: An algorithm for real-time vessel enhancement and detection. Comput. Methods Programs Biomed. 52, 1–22 (1996)CrossRefGoogle Scholar
  10. 10.
    Rosenblatt F.: The perceptron: a probabilistic model for information storage and organization in the brain. Psychol. Rev. 65(6), 386–408 (1958)CrossRefMathSciNetGoogle Scholar
  11. 11.
    Schalkoff R.J.: Pattern Recognition: Statistical, Structural and Neural Approaches. Wiley, New York (1992)Google Scholar
  12. 12.
    Steger C.: An unbiased detector of curvilinear structures. IEEE Trans. Pattern Anal. Mach. Intell. 20(2), 113–125 (1998)CrossRefGoogle Scholar
  13. 13.
    Stoica R., Descombes X., Zerubia J.: A Gibbs point process for road extraction from remotely sensed images. Int. J Comput. Vis. 57(2), 121–136 (2004)CrossRefGoogle Scholar
  14. 14.
    Upchurch D.R., Ritchie J.T.: Root observations using a video recording system in mini-rhizotrons. Agron. J. 75(6), 1009–1015 (1983)Google Scholar
  15. 15.
    Vamerali T., Ganis A., Bona S., Mosca G.: An approach to minirhizotron root image analysis. Plant Soil 217(1), 183–193 (1999)CrossRefGoogle Scholar
  16. 16.
    van Lieshout M.N.M.: Markov Point Processes and their Applications. Imperial College Press, London (2000)zbMATHCrossRefGoogle Scholar
  17. 17.
    Voorhees W.B., Carlson V.A., Hallauert E.A.: Root length measurement with a computer-controlled digital scanning micro-densitometer. Agron. J. 72, 847–851 (1980)CrossRefGoogle Scholar
  18. 18.
    Zeng G., Birchfield S.T., 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
  19. 19.
    Zeng G., Birchfield S.T., Wells C.E.: Automatic discrimination of fine roots in minirhizotron images. New Phytol. 177(2), 549–557 (2008)Google Scholar

Copyright information

© Springer-Verlag 2008

Authors and Affiliations

  • Guang Zeng
    • 1
  • Stanley T. Birchfield
    • 1
  • Christina E. Wells
    • 2
  1. 1.Department of Electrical and Computer EngineeringClemson UniversityClemsonUSA
  2. 2.Department of HorticultureClemson UniversityClemsonUSA

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