Top

Machine Vision and Applications

Published in:

01-07-2016 | Special Issue Paper

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

Authors: Marco Augustin, Yll Haxhimusa, Wolfgang Busch, Walter G. Kropatsch

Published in: Machine Vision and Applications | Issue 5/2016

Activate our intelligent search to find suitable subject content or patents.

search-config

AI-assisted search

Off

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.

previous article Automated estimation of tiller number in wheat by ribbon detection

next article Validation of plant part measurements using a 3D reconstruction method suitable for high-throughput seedling phenotyping

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

über 102.000 Bücher
über 537 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Finance + Banking
Management + Führung
Marketing + Vertrieb
Maschinenbau + Werkstoffe
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 340 Zeitschriften

aus folgenden Fachgebieten:

Bauwesen + Immobilien
Business IT + Informatik
Finance + Banking
Management + Führung
Marketing + Vertrieb
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 390 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Maschinenbau + Werkstoffe

Jetzt Wissensvorsprung sichern!

inform now

http://www.mathworks.com/products/matlab/.

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)CrossRef

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)CrossRef

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)CrossRef

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)

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)CrossRef

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)

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)

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

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)CrossRef

10.

Bresenham, J.E.: Algorithm for computer control of a digital plotter. IBM Syst. J. 4(1), 25–30 (1965)CrossRef

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)CrossRef

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)

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)CrossRef

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)CrossRef

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)CrossRef

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)CrossRef

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)CrossRef

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)CrossRef

19.

The Arabidopsis Genome Initiative: Analysis of the genome sequence of the flowering plant Arabidopsis thaliana. Nature 408(6814), 796–815 (2000)

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)

21.

Li, L., Zhang, Q., Huang, D.: A review of imaging techniques for plant phenotyping. Sensors 14(11), 20078–20111 (2014)CrossRef

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)CrossRef

23.

Lobet, G., Draye, X., Périlleux, C.: An online database for plant image analysis software tools. Plant Methods 9(38), 1–7 (2013)CrossRef

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)

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)

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)CrossRef

27.

Meijering, E.: Neuron tracing in perspective. Cytom. Part A 77A(7), 693–704 (2010)MathSciNetCrossRef

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)CrossRef

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)

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.

Mutka, A., Bart, R.: Image-based phenotyping of plant disease symptoms. Front. Plant Sci. 5, 734 (2015). doi:10.3389/fpls.2014.00734 CrossRef

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)

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)CrossRef

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)

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)CrossRef

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)

37.

Rousseeuw, P.J., Leroy, A.M.: Robust Regression and Outlier Detection. Wiley, New York (1987)CrossRefMATH

38.

Rousseeuw, P.J., Driessen, K.V.: A fast algorithm for the minimum covariance determinant estimator. Technometrics 41(3), 212–223 (1999)CrossRef

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)CrossRef

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)

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)CrossRef

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)CrossRef

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)CrossRef

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)CrossRef

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)CrossRef

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.

Weigel, D.: Natural variation in arabidopsis: from molecular genetics to ecological genomics. Plant Physiol. 158(1), 2–22 (2012)MathSciNetCrossRef

48.

Yin, Y., Adel, M., Bourennane, S.: Retinal vessel segmentation using a probabilistic tracking method. Pattern Recogn. 45(4), 1235–1244 (2012)CrossRefMATH

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)CrossRefMATH

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)CrossRef

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)

Title: A framework for the extraction of quantitative traits from 2D images of mature Arabidopsis thaliana
Authors: Marco Augustin
Yll Haxhimusa
Wolfgang Busch
Walter G. Kropatsch
Publication date: 01-07-2016
Publisher: Springer Berlin Heidelberg
Published in: Machine Vision and Applications / Issue 5/2016
Print ISSN: 0932-8092
Electronic ISSN: 1432-1769
DOI: https://doi.org/10.1007/s00138-015-0720-z

Springer Professional

Abstract

Please log in to get access to your license.

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft+Technik"

Springer Professional "Wirtschaft"

Springer Professional "Technik"

Other articles of this Issue 5/2016

Finding local leaf vein patterns for legume characterization and classification

Generation and application of hyperspectral 3D plant models: methods and challenges

Special issue on computer vision and image analysis in plant phenotyping

A patch-based approach to 3D plant shoot phenotyping

Validation of plant part measurements using a 3D reconstruction method suitable for high-throughput seedling phenotyping

A novel Computer-Aided Tree Species Identification method based on Burst Wind Segmentation of 3D bark textures

Premium Partner