Multiscale imaging of plants: Current approaches and challenges

Plant Methods

Volumn 11, Issue 1, 2015, Pages

  Rousseau, David ^a   Chéné, Yann ^b   Belin, Etienne ^b   Semaan, Georges ^b   Trigui, Ghassen ^c   Boudehri, Karima ^c   Franconi, Florence ^d   Chapeau Blondeau, François ^b  

^a UNIVERSITÉ DE LYON   (France)

^b UNIVERSITÉ D'ANGERS   (France)

^c GEVES   (France)

^d UNIVERSITÉ D'ANGERS   (France)

Author keywords

Fractal; ImageJ plugins; Multiscale filtering; Mutiscale imaging; Wavelets

Indexed keywords

EID: 84924245200     PISSN: None     EISSN: 17464811     Source Type: Journal    
DOI: 10.1186/s13007-015-0050-1     Document Type: Review

References (75)

1. Prusinkiewicz P, Lindenmayer A. The Algorithmic Beauty of Plants. Berlin: Springer; 2004.
2. Godin C. Representing and encoding plant architecture: a review. Ann Forest Sci. 2000; 57(5):413-38.
3. Leitner D, Klepsch S, Bodner G, Schnepf A. A dynamic root system growth model based on L-systems. Plant and Soil. 2010; 332(1-2):177-92.
4. Benoit L, Rousseau D, Belin É, Demilly D, Chapeau-Blondeau F. Simulation of image acquisition in machine vision dedicated to seedling elongation to validate image processing root segmentation algorithms. Comput Electron Agric. 2014; 104:84-92.
5. Dufour-Kowalski S, Pradal C, Donès N, Barbier De Reuille P, Boudon F, Chopard J, et al. OpenAlea: An open-software plateform for the integration of heterogenous FSPM components. In: 5th International Workshop on Functional-Structural Plant Models, FSPM07, November, 2007. Napier, New Zealand: The Horticulture and Food Research Institute of New Zealand Ltd.: 2007. p. 1-2.
6. Mirabet V, Das P, Boudaoud A, Hamant O. The role of mechanical forces in plant morphogenesis. Annu Rev Plant Biol. 2011; 62:365-85.
7. Lee AB, Mumford D, Huang J. Occlusion models for natural images: A statistical study of a scale-invariant dead leaves model. International Journal of Computer Vision. 2001; 41:35-59.
8. Bordenave C, Gousseau Y, Roueff F. The dead leaves model: A general tessellation modeling occlusion. Adv Appl Probability. 2006; 38:31-46.
9. Gousseau Y, Roueff F. Modeling occlusion and scaling in natural images. SIAM Journal of Multiscale Modeling and Simulation. 2007; 6:105-34.
10. Langhans M, Meckel T. Single-molecule detection and tracking in plants. Protoplasma. 2014; 251(2):277-91.
11. Elgass K, Caesar K, Schleifenbaum F, Stierhof Y-D, Meixner AJ, Harter K. Novel application of fluorescence lifetime and fluorescence microscopy enables quantitative access to subcellular dynamics in plant cells. PLoS One. 2009; 4(5):5716.
12. Gutierrez R, Grossmann G, Frommer WB, Ehrhardt DW. Opportunities to explore plant membrane organization with super-resolution microscopy. Plant Physiol. 2010; 154(2):463-6.
13. Sparkes I, Graumann K, Martinière A, Schoberer J, Wang P, Osterrieder A. Bleach it, switch it, bounce it, pull it: using lasers to reveal plant cell dynamics. J Exp Bot. 2010:351. doi:10.1093/jxb/erq351.
14. Fitzgibbon J, Bell K, King E, Oparka K. Super-resolution imaging of plasmodesmata using three-dimensional structured illumination microscopy. Plant Physiol. 2010; 153(4):1453-63.
15. Wan Y, Ash WM, Fan L, Hao H, Kim MK, Lin J. Variable-angle total internal reflection fluorescence microscopy of intact cells of Arabidopsis thaliana. Plant Methods. 2011; 7(1):27.
16. Cloetens P, Mache R, Schlenker M, Lerbs-Mache S. Quantitative phase tomography of Arabidopsis seeds reveals intercellular void network. Proc Nat Acad Sci USA. 2006; 103(39):14626-30.
17. Costa A, Candeo A, Fieramonti L, Valentini G, Bassi A. Calcium dynamics in root cells of Arabidopsis thaliana visualized with selective plane illumination microscopy. PLoS ONE. 2013; 8:75646.
18. Fernandez R, Das P, Mirabet V, Moscardi E, Traas J, Verdeil J, et al. Imaging plant growth in 4D: robust tissue reconstruction and lineaging at cell resolution. Nat Methods. 2010; 7:547-53.
19. Leea K, Avondob J, Morrisonc H, Blotb L, Starkd M, Sharpec J, et al. Visualizing plant development and gene expression in three dimensions using optical projection tomography. Plant Cell. 2006; 18:2145-56.
20. Mairhofer S, Zappala S, Tracy SR, Sturrock C, Bennett M, Mooney SJ, et al. RooTrak: automated recovery of three-dimensional plant root architecture in soil from X-ray microcomputed tomography images using visual tracking. Plant Physiol. 2012; 158(2):561-9.
21. Zappala S, Mairhofer S, Tracy S, Sturrock CJ, Bennett M, Pridmore T, et al. Quantifying the effect of soil moisture content on segmenting root system architecture in X-ray computed tomography images. Plant and Soil. 2013; 370(1-2):35-45.
22. Mairhofer S, Zappala S, Tracy S, Sturrock C, Bennett MJ, Mooney SJ, et al. Recovering complete plant root system architectures from soil via X-ray micro-computed tomography. Plant Methods. 2013; 9(8):1-7.
23. Salon C, Jeudy C, Bernard C, Mougel C, Coffin A, Bourion V, et al. Google Patents. EP Patent App. 2014:EP20,130,173,626. http://www.google.com/patents/EP2679088A1?cl=fr.
24. Dinneny JR. Luciferase Reporter System for Roots and Methods of Using the Same. Google Patents. US Patent App. 2014; 13/970:960. http://www.google.com/patents/US20140051101.
25. Rellán Álvarez R. Growth and luminescence observatory for roots (glo-roots): A platform for the analysis of root structure and physiology in soil. In: Plant and Animal Genome XXII Conference. Plant and Animal Genome: 2014.
26. Chéné Y, Belin E, Chapeau-Blondeau F, Boureau T, Caffier V, Rousseau D. Anatomo-functional bimodality imaging for plant phenotyping: An insight through depth imaging coupled to thermal imaging. In: Plant Image Analysis: Fundamentals and Applications. Boca Raton: CRC Press: 2014. Chap. 9.
27. Paulus S, Behmann J, Mahlein A-K, Plümer L, Kuhlmann H. Low-cost 3d systems: Suitable tools for plant phenotyping. Sensors. 2014; 14(2):3001-18.
28. Jones HG, Vaughan RA. Remote Sensing of Vegetation: Principles, Techniques, and Applications. Oxford: Oxford University Press; 2010.
29. Zarco-Tejada PJ, González-Dugo V, Berni JA. Fluorescence, temperature and narrow-band indices acquired from a UAV platform for water stress detection using a micro-hyperspectral imager and a thermal camera. Remote Sensing Environ. 2012; 117:322-37.
30. Hettinger JW, de la Pena Mattozzi M, Myers WR, Williams ME, Reeves A, Parsons RL, et al. Optical coherence microscopy. a technology for rapid, in vivo, non-destructive visualization of plants and plant cells. Plant Physiol. 2000; 123(1):3-16.
31. Meglinski I, Buranachai C, Terry L. Plant photonics: application of optical coherence tomography to monitor defects and rots in onion. Laser Phys Lett. 2010; 7(4):307.
32. Dhondt S, Wuyts N, Inzé D. Cell to whole-plant phenotyping: the best is yet to come. Trends Plant Sci. 2013; 18(8):428-39.
33. Ehrhardt DW, Frommer WB. New technologies for 21st century plant science. The Plant Cell Online. 2012; 24(2):374-94.
34. Sozzani R, Busch W, Spalding EP, Benfey PN. Advanced imaging techniques for the study of plant growth and development. Trends in Plant Sci. 2014; 19(5):304-10.
35. Benoit L, Belin É, Durr C, Chapeau-Blondeau F, Demilly D, Ducournau S, et al. Computer vision under inactinic light for hypocotyl-radicle separation with a generic gravitropism-based criterion. Comput Electron Agric. 2015; 111:12-7.
36. Bell K, Mitchell S, Paultre D, Posch M, Oparka K. Correlative imaging of fluorescent proteins in resin-embedded plant material1. Plant Physiol. 2013; 161(4):1595-603.
37. Jahnke S, Menzel MI, Van Dusschoten D, Roeb GW, Bühler J, Minwuyelet S, et al. Combined mri-pet dissects dynamic changes in plant structures and functions. The Plant J. 2009; 59(4):634-44.
38. Zitova B, Flusser J. Image registration methods: a survey. Image Vision Comput. 2003; 21(11):977-1000.
39. Lowe DG. Distinctive image features from scale-invariant keypoints. Int J Comput Vision. 2004; 60(2):91-110.
40. Dale MRT, Mah M. The use of wavelets for spatial pattern analysis in ecology. Journal of Vegetation Science. 1998; 9(6):805-14.
41. Gu X, Du J-X, Wang X-F. Leaf recognition based on the combination of wavelet transform and Gaussian interpolation. In: Advances In Intelligent Computing. Berlin: Springer: 2005. p. 253-62.
42. Prasad S, Kumar P, Tripathi R. Plant leaf species identification using curvelet transform. In: 2nd International Conference on Computer and Communication Technology (ICCCT). IEEE: 2011. p. 646-52.
43. Casanova D, de Mesquita Sa Junior JJ, Martinez Bruno O. Plant leaf identification using Gabor wavelets. Int J Imaging Sys Technol. 2009; 19(3):236-43.
44. Bours R, Muthuraman M, Bouwmeester H, van der Krol A. Oscillator: A system for analysis of diurnal leaf growth using infrared photography combined with wavelet transformation. Plant Methods. 2012; 8(1):29.
45. Epinat V, Stein A, de Jong SM, Bouma J. A wavelet characterization of high-resolution NDVI patterns for precision agriculture. Int J Appl Earth Observation Geoinformation. 2001; 3(2):121-32.
46. Strand EK, Smith AM, Bunting SC, Vierling LA, Hann DB, Gessler PE. Wavelet estimation of plant spatial patterns in multitemporal aerial photography. Int J Remote Sensing. 2006; 27(10):2049-54.
47. Frangi AF, Niessen WJ, Vincken KL, Viergever MA. Multiscale vessel enhancement filtering. In: Medical Image Computing and Computer-Assisted Interventation-MICCAI'98. Berlin: Springer: 1998. p. 130-7.
48. Sage D, Neumann FR, Hediger F, Gasser SM, Unser M. Automatic tracking of individual fluorescence particles: Application to the study of chromosome dynamics. IEEE Trans Image Process. 2005; 14:1372-83.
49. Huang NE, Shen Z, Long SR, Wu MC, Shih HH, Zheng Q, et al. The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis. Proc R Soc London Ser A: Math Phys Eng Sci. 1998; 454(1971):903-95.
50. Nunes JC, Bouaoune Y, Delechelle E, Niang O, Bunel P. Image analysis by bidimensional empirical mode decomposition. Image Vision Comput. 2003; 21(12):1019-26.
51. Nunes JC, Guyot S, Deléchelle E. Texture analysis based on local analysis of the bidimensional empirical mode decomposition. Machine Vision Appl. 2005; 16(3):177-88.
52. Myers WL, Patil GP. Pattern-Based Compression of Multi-Band Image Data for Landscape Analysis. Berlin: Springer; 2006.
53. Buades A, Coll B, Morel J-M. A review of image denoising algorithms, with a new one. Multiscale Model Simul. 2005; 4(2):490-530.
54. Buades A, Coll B, Morel J-M. A non-local algorithm for image denoising. In: Computer Vision and Pattern Recognition, 2005. CVPR 2005. IEEE Computer Society Conference On. vol. 2. IEEE: 2005. p. 60-5.
55. Palmer MW. Fractal geometry: A tool for describing spatial patterns of plant communities. Vegetatio. 1988; 75:91-102.
56. Critten DL. Fractal dimension relationships and values associated with certain plant canopies. J Agric Eng Res. 1997; 67:61-72.
57. Alados CL, Escos J, Emlen JM, Freeman DC. Characterization of branch complexity by fractal analyses. Int J Plant Sci. 1999; 160:147-55.
58. Morávek Z, Fiala J. Fractal dynamics in the growth of root. Chaos, Solitons & Fractals. 2004; 19:31-4.
59. Alados CL, Pueyo Y, Navas D, Cabezudo B, Gonzalez A, Freeman DC. Fractal analysis of plant spatial patterns: A monitoring tool for vegetation transition shifts. Biodivers Conserv. 2005; 14:1453-68.
60. Ruderman DL, Bialek W. Statistics of natural images: Scaling in the woods. Phys Rev Lett. 1994; 73:814-7.
61. Ruderman DL. Origins of scaling in natural images. Vision Res. 1997; 37(23):3385-98.
62. Chauveau J, Rousseau D, Richard P, Chapeau-Blondeau F. Multifractal analysis of three-dimensional histogram from color images. Chaos, Solitons & Fractals. 2010; 43(1):57-67.
63. Chauveau J, Rousseau D, Chapeau-Blondeau F. Fractal capacity dimension of three-dimensional histogram from color images. Multidimensional Syst Signal Process. 2010; 21(2):197-211.
64. Chapeau-Blondeau F, Chauveau J, Rousseau D, Richard P. Fractal structure in the color distribution of natural images. Chaos, Solitons & Fractals. 2009; 42(1):472-82.
65. Chéné Y, Belin E, Rousseau D, Chapeau-Blondeau F. Multiscale analysis of depth images from natural scenes: Scaling in the depth of the woods. Chaos, Solitons & Fractals. 2013; 54:135-49.
66. Ferraro P, Godin C, Prusinkiewicz P. Toward a quantification of self-similarity in plants. Fractals. 2005; 13:91-109.
67. Martinez Bruno O, de Oliveira Plotze R, Falvo M, de Castro M. Fractal dimension applied to plant identification. Inf Sci. 2008; 178:2722-33.
68. Da Silva D, Boudon F, Godin C, Sinoquet H. Multiscale framework for modeling and analyzing light interception by trees. Multiscale Model Simul. 2008; 7:910-33.
69. Chéné Y, Rousseau D, Lucidarme P, Bertheloot J, Caffier V, Morel P, et al. On the use of depth camera for 3D phenotyping of entire plants. Comput Electron Agric. 2012; 82:122-7.
70. Chandra M, Rani M. Categorization of fractal plants. Chaos Solitons & Fractals. 2009; 41(3):1442-7.
71. Scheuring I, Riedi RH. Application of multifractals to the analysis of vegetation pattern. J Vegetation Sci. 1994; 5(4):489-496.
72. Bradshaw G, Spies TA. Characterizing canopy gap structure in forests using wavelet analysis. J Ecol. 1992; 80:205-15.
73. Tastumi J, Yamauchi A, Kono Y. Fractal analysis of plant root systems. Ann Bot. 1989; 64(5):499-503.
74. Izumi Y, Iijima M. Fractal and multifractal analysis of cassava root system grown by the root-box method. Plant Production Sci. 2002; 5(2):146-51.
75. Ketipearachchi KW, Tatsumi J. Local fractal dimensions and multifractal analysis of the root system of legumes. Plant Production Sci. 2000; 3(3):289-95.