Advertisement

Machine Vision and Applications

, Volume 27, Issue 5, pp 647–661 | Cite as

A framework for the extraction of quantitative traits from 2D images of mature Arabidopsis thaliana

  • Marco Augustin
  • Yll Haxhimusa
  • Wolfgang Busch
  • Walter G. Kropatsch
Special Issue Paper

Abstract

In this work, we propose an image-based phenotyping framework for the determination of quantitative traits from mature Arabidopsis thaliana plants. Two-dimensional (2D) images taken from the dried and flattened plants are analyzed regarding their geometry as well as their branching topology. The realistic branching architecture is hereby reconstructed from a single 2D image using a tracing approach with a semi-circular search window. The centerline segments of the tracing procedure are subsequently merged and labeled based on a hierarchical approach combining continuity properties with geometrical and topological information determined during tracing. This paper covers a detailed description of the proposed plant phenotyping pipeline from the image acquisition process until the extraction of the quantitative traits. The framework is evaluated using a set of 106 images and compared to a manual phenotyping approach as well as a semi-automatic image-based approach. The most relevant results of this evaluation are presented.

Keywords

Image-based phenotyping Geometrical traits Topological traits Tracing Hierarchical reconstruction  Network of curvilinear structures 

Notes

Acknowledgments

We want to thank Svante Holm (Mid Sweden University, SE) and Alison Anastasio (University of Chicago, US) for planting and harvesting the plants, Man Yu and Andrew Davis for taking the photos and Benjamin Brachi (Bergelson Lab, University of Chicago, US) for his valuable inputs and support along the stages of development.

References

  1. 1.
    Al-Tam, F., Adam, H., Anjos, A., Lorieux, M., Larmande, P., Ghesquiere, A., Jouannic, S., Shahbazkia, H.: P-TRAP: a panicle trait phenotyping tool. BMC Plant Biol. 13(1), 122 (2013)CrossRefGoogle Scholar
  2. 2.
    Armengaud, P., Zambaux, K., Hills, A., Sulpice, R., Pattison, R.J., Blatt, M.R., Amtmann, A.: EZ-Rhizo: integrated software for the fast and accurate measurement of root system architecture. Plant J. 57(5), 945–956 (2009)CrossRefGoogle Scholar
  3. 3.
    Arvidsson, S., Perez-Rodriguez, P., Mueller-Roeber, B.: A growth phenotyping pipeline for Arabidopsis thaliana integrating image analysis and rosette area modeling for robust quantification of genotype effects. New Phytol. 191(3), 895–907 (2011)CrossRefGoogle Scholar
  4. 4.
    Augustin, M., Haxhimusa, Y., Busch, W., Kropatsch, W.G.: Image-based phenotyping of the mature Arabidopsis shoot system. In: Agapito, L., Bronstein, M.M., Rother, C. (eds.) Computer Vision—ECCV 2014 Workshops. Lecture Notes in Computer Science, vol. 8928, pp. 231–246. Springer, Berlin (2015)Google Scholar
  5. 5.
    Basu, P., Pal, A., Lynch, J.P., Brown, K.M.: A novel image-analysis technique for kinematic study of growth and curvature. Plant Physiol. 145(2), 305–316 (2007)CrossRefGoogle Scholar
  6. 6.
    Benmansour, F., Fua, P., Türetken, E.: Automated reconstruction of tree structures using path classifiers and mixed integer programming. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 566–573 (2012)Google Scholar
  7. 7.
    Boroujeni, F.Z., Wirza, R., Rahmat, O., Mustapha, N., Affendey, L.S., Maskon, O.: Automatic selection of initial points for exploratory vessel tracing in fluoroscopic images. Def. Sci. J. 61, 443–451 (2011)Google Scholar
  8. 8.
    Boroujeni, F.Z., Rahmat, O., Wirza, R., Mustapha, N., Affendey, L.S., Maskon, O.: Coronary artery center-line extraction using second order local features. Comput. Math. Methods Med. 2012 (2012). doi: 10.1155/2012/940981
  9. 9.
    Brachi, B., Morris, G.P., Borevitz, J.O.: Genome-wide association studies in plants: the missing heritability is in the field. Genome Biol. 12(10), 232 (2011)CrossRefGoogle Scholar
  10. 10.
    Bresenham, J.E.: Algorithm for computer control of a digital plotter. IBM Syst. J. 4(1), 25–30 (1965)CrossRefGoogle Scholar
  11. 11.
    Cobb, J.N., DeClerck, G., Greenberg, A., Clark, R., McCouch, S.: Next-generation phenotyping: requirements and strategies for enhancing our understanding of genotype–phenotype relationships and its relevance to crop improvement. Theor. Appl. Genet. 126(4), 867–887 (2013)CrossRefGoogle Scholar
  12. 12.
    Crowell, S., Falcão, A.X., Shah, A., Wilson, Z., Greenberg, A.J., McCouch, S.R.: High-resolution inflorescence phenotyping using a novel image-analysis pipeline, panorama. Plant Physiol. 165(2), 479–495 (2014)Google Scholar
  13. 13.
    Delibasis, K.K., Kechriniotis, A.I., Tsonos, C., Assimakis, N.: Automatic model-based tracing algorithm for vessel segmentation and diameter estimation. Comput. Methods Programs Biomed. 100(2), 108–122 (2010)CrossRefGoogle Scholar
  14. 14.
    Fraz, M.M., Remagnino, P., Hoppe, A., Uyyanonvara, B., Rudnicka, A.R., Owen, C.G., Barman, S.A.: Blood vessel segmentation methodologies in retinal images—a survey. Comput. Methods Programs Biomed. 108(1), 407–433 (2012)CrossRefGoogle Scholar
  15. 15.
    French, A.P., Ubeda-Tomas, S., Holman, T., Bennett, M., Pridmore, T.: High-throughput quantification of root growth using a novel image-analysis tool. Plant Physiol. 150(4), 1784–1795 (2009)CrossRefGoogle Scholar
  16. 16.
    Godin, C., Costes, E., Sinoquet, H.: A method for describing plant architecture which integrates topology and geometry. Ann. Botany 84(3), 343–357 (1999)CrossRefGoogle Scholar
  17. 17.
    Huang, Y., Zhang, J., Huang, Y.: An automated computational framework for retinal vascular network labeling and branching order analysis. Microvascu. Res. 84(2), 169–177 (2012)CrossRefGoogle Scholar
  18. 18.
    Humplík, J.F., Lazár, D., Fürst, T., Husičková, A., Hýbl, M., Spíchal, L.: Automated integrative high-throughput phenotyping of plant shoots: a case study of the cold-tolerance of pea ( Pisum sativum L.). Plant Methods 11(1), 1–11 (2015)CrossRefGoogle Scholar
  19. 19.
    The Arabidopsis Genome Initiative: Analysis of the genome sequence of the flowering plant Arabidopsis thaliana. Nature 408(6814), 796–815 (2000)Google Scholar
  20. 20.
    Klodt, M., Cremers, D.: High-resolution plant shape measurements from multi-view stereo reconstruction. In: Agapito, L., Bronstein, M.M., Rother, C. (eds.) Computer Vision—ECCV 2014 Workshops. Lecture Notes in Computer Science, vol. 8928, pp. 174–184. Springer, Berlin (2015)Google Scholar
  21. 21.
    Li, L., Zhang, Q., Huang, D.: A review of imaging techniques for plant phenotyping. Sensors 14(11), 20078–20111 (2014)CrossRefGoogle Scholar
  22. 22.
    Lin, K.S., Tsai, C.L., Tsai, C.H., Sofka, M., Chen, S.J., Lin, W.Y.: Retinal vascular tree reconstruction with anatomical realism. IEEE Trans. Biomed. Eng. 59(12), 3337–3347 (2012)CrossRefGoogle Scholar
  23. 23.
    Lobet, G., Draye, X., Périlleux, C.: An online database for plant image analysis software tools. Plant Methods 9(38), 1–7 (2013)CrossRefGoogle Scholar
  24. 24.
    Longair, M.H., Baker, D.A., Armstrong, J.D.: Simple neurite tracer: Open source software for reconstruction, visualization and analysis of neuronal processes. Bioinformatics 27(17), 2453–2454 (2011)Google Scholar
  25. 25.
    Martinez-Perez, M.E., Hughes, A.D., Stanton, A.V., Thom, S.A., Bharath, A.A., Parker, K.H.: Retinal blood vessel segmentation by means of scale-space analysis and region growing. In: International Conference on Medical Image Computing and Computer Assisted Intervention, pp. 90–97 (1999)Google Scholar
  26. 26.
    Martinez-Perez, M.E., Hughes, A.D., Stanton, A.V., Thom, S.A., Chapman, N., Bharath, A.A., Parker, K.H.: Retinal vascular tree morphology: a semi-automatic quantification. IEEE Trans. Biomed. Eng. 49(8), 912–917 (2002)CrossRefGoogle Scholar
  27. 27.
    Meijering, E.: Neuron tracing in perspective. Cytom. Part A 77A(7), 693–704 (2010)MathSciNetCrossRefGoogle Scholar
  28. 28.
    Minervini, M., Abdelsamea, M.M., Tsaftaris, S.A.: Image-based plant phenotyping with incremental learning and active contours. Ecol. Inform. 23, 35–48 (2014)CrossRefGoogle Scholar
  29. 29.
    Minervini, M., Giuffrida, M.V., Tsaftaris, S.: An interactive tool for semi-automated leaf annotation. In: Tsaftaris, S.A., Scharr, H., Pridmore, T. (eds.) Proceedings of the Computer Vision Problems in Plant Phenotyping (CVPPP), pp. 6.1-6.13. BMVA Press (2015)Google Scholar
  30. 30.
    Müller-Linow, M., Pinto-Espinosa, F., Scharr, H., Rascher, U.: The leaf angle distribution of natural plant populations: assessing the canopy with a novel software tool. Plant Methods 11(1) (2015). doi: 10.1186/s13007-015-0052-z
  31. 31.
    Mutka, A., Bart, R.: Image-based phenotyping of plant disease symptoms. Front. Plant Sci. 5, 734 (2015). doi: 10.3389/fpls.2014.00734 CrossRefGoogle Scholar
  32. 32.
    Naeem, A., French, A.P., Wells, D.M., Pridmore, T.: High-throughput feature counting and measurement of roots. Bioinformatics 27(9), 1337–1338 (2011)Google Scholar
  33. 33.
    Nguyen, U.T.V., Bhuiyan, A., Park, L.A.F., Ramamohanarao, K.: An effective retinal blood vessel segmentation method using multi-scale line detection. Pattern Recogn. 46(3), 703–715 (2013)CrossRefGoogle Scholar
  34. 34.
    Pape, J.M., Klukas, C.: 3-D histogram-based segmentation and leaf detection for rosette plants. In: Agapito, L., Bronstein, M.M., Rother, C. (eds.) Computer Vision—ECCV 2014 Workshops. Lecture Notes in Computer Science, vol. 8928, pp. 61–74. Springer, Berlin (2015)Google Scholar
  35. 35.
    Pound, M.P., French, A.P., Murchie, E.H., Pridmore, T.P.: Automated recovery of three-dimensional models of plant shoots from multiple color images. Plant Physiol. 166(4), 1688–1698 (2014)CrossRefGoogle Scholar
  36. 36.
    Robben, D., Türetken, E., Sunaert, S.: Simultaneous segmentation and anatomical labeling of the cerebral vasculature. In: International Conference on Medical Image Computing and Computer Assisted Intervention (2014)Google Scholar
  37. 37.
    Rousseeuw, P.J., Leroy, A.M.: Robust Regression and Outlier Detection. Wiley, New York (1987)CrossRefzbMATHGoogle Scholar
  38. 38.
    Rousseeuw, P.J., Driessen, K.V.: A fast algorithm for the minimum covariance determinant estimator. Technometrics 41(3), 212–223 (1999)CrossRefGoogle Scholar
  39. 39.
    Rousseau, D., Chéné, Y., Belin, E., Semaan, G., Trigui, G., Boudehri, K., Franconi, F., Chapeau-Blondeau, F.: Multiscale imaging of plants: current approaches and challenges. Plant Methods 11(1), 1–9 (2015)CrossRefGoogle Scholar
  40. 40.
    Slovak, R., Göschl, C., Su, X., Shimotani, K., Shiina, T., Busch, W.: A scalable open-source pipeline for large-scale root phenotyping of Arabidopsis. Plant Cell 26(6), 2390–2403 (2014)Google Scholar
  41. 41.
    Sozzani, R., Benfey, P.: High-throughput phenotyping of multicellular organisms: finding the link between genotype and phenotype. Genome Biol. 12(3), 219–225 (2011)CrossRefGoogle Scholar
  42. 42.
    Spalding, E.P., Miller, N.D.: Image analysis is driving a renaissance in growth measurement. Curr. Opin. Plant Biol. 16(1), 100–104 (2013)CrossRefGoogle Scholar
  43. 43.
    Subramanian, R., Spalding, E.P., Ferrier, N.J.: A high throughput robot system for machine vision based plant phenotype studies. Mach. Vis. Appl. 24(3), 619–636 (2013)CrossRefGoogle Scholar
  44. 44.
    Sun, Y.: Automated identification of vessel contours in coronary arteriograms by an adaptive tracking algorithm. IEEE Trans. Med. Imaging 8(1), 78–88 (1989)CrossRefGoogle Scholar
  45. 45.
    Türetken, E., Gonzalez, G., Blum, C., Fua, P.: Automated reconstruction of dendritic and axonal trees by global optimization with geometric priors. Neuroinformatics 9(2–3), 279–302 (2011)CrossRefGoogle Scholar
  46. 46.
    Walter, A., Liebisch, F., Hund, A.: Plant phenotyping: from bean weighing to image analysis. Plant Methods 11(1) (2015). doi: 10.1186/s13007-015-0056-8
  47. 47.
    Weigel, D.: Natural variation in arabidopsis: from molecular genetics to ecological genomics. Plant Physiol. 158(1), 2–22 (2012)MathSciNetCrossRefGoogle Scholar
  48. 48.
    Yin, Y., Adel, M., Bourennane, S.: Retinal vessel segmentation using a probabilistic tracking method. Pattern Recogn. 45(4), 1235–1244 (2012)CrossRefzbMATHGoogle Scholar
  49. 49.
    Zana, F., Klein, J.C.: Segmentation of vessel-like patterns using mathematical morphology and curvature evaluation. Trans. Image Process. 10(7), 1010–1019 (2001)CrossRefzbMATHGoogle Scholar
  50. 50.
    Zhang, Y., Zhou, X., Degterev, A., Lipinski, M., Adjeroh, D., Yuan, J., Wong, S.: A novel tracing algorithm for high throughput imaging screening of neuron-based assays. J. Neurosci. Methods 160(1), 149–62 (2007)CrossRefGoogle Scholar
  51. 51.
    Zheng, Y., Gu, S., Edelsbrunner, H., Tomasi, C., Benfey, P.: Detailed reconstruction of 3D plant root shape. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2026–2033 (2011)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2015

Authors and Affiliations

  1. 1.Center for Medical Physics and Biomedical EngineeringMedical University of ViennaViennaAustria
  2. 2.Center for Medical Statistics, Informatics and Intelligent SystemsMedical University of ViennaViennaAustria
  3. 3.Faculty of Electrical and Computer EngineeringUniversity of PrishtinaPrishtinaKosovo
  4. 4.Gregor Mendel Institute of Molecular Plant BiologyAustrian Academy of SciencesViennaAustria
  5. 5.Pattern Recognition and Image Processing GroupVienna University of TechnologyViennaAustria

Personalised recommendations