Automatic identification and characterization of radial files in light microscopy images of wood

Annals of Botany

Volumn 114, Issue 4, 2014, Pages 829-840

  Brunel, Guilhem ^a,b   Borianne, Philippe ^b   Subsol, Gerard ^c   Jaeger, Marc ^b   Caraglio, Yves ^b  

^a UMR AMAP   (France)

^b UNIV MONTPELLIER   (France)

^c LIRMM   (France)

Author keywords

Abies alba; cellular structure; Functional structural plant modelling; image processing; light microscopy; microscopic wood images; P. nigra; Pinus canariensis; radial cell file; reliability coefficients; Swietenia

Indexed keywords

ALGORITHM; BIOINDICATOR; CELL ORGANELLE; CONIFEROUS TREE; HISTOLOGY; IDENTIFICATION METHOD; IMAGE PROCESSING; MICROSCOPY; WOOD;

ABIES ALBA; PINUS CANARIENSIS; SWIETENIA;

ABIES; ALGORITHM; ANATOMY AND HISTOLOGY; IMAGE PROCESSING; MELIACEAE; MICROSCOPY; PINE; PROCEDURES; REPRODUCIBILITY; TREE; WOOD;

ABIES; ALGORITHMS; IMAGE PROCESSING, COMPUTER-ASSISTED; MELIACEAE; MICROSCOPY; PINUS; REPRODUCIBILITY OF RESULTS; TREES; WOOD;

EID: 84910616021     PISSN: 03057364     EISSN: 10958290     Source Type: Journal    
DOI: 10.1093/aob/mcu119     Document Type: Article

References (41)

1. Andrès E. 1994. Discrete circles, rings and spheres.Computers and Graphics 18: 695-706
2. Baggett D, Nakaya M, McAuliffe M, Yamaguchi TP, Lockett S. 2005. Whole cell segmentation in solid tissue sections. Cytometry. Part A: The Journal of the International Society for Analytical Cytology 67: 137-143
3. Barthélémy D, Caraglio Y. 2007. Plant architecture: a dynamic, multilevel and comprehensive approach to plant form, structure and ontogeny. Annals of Botany 99: 375-407
4. Beucher S. 2012. Towards a unification ofwaterfalls, standard andPalgorithms. CMM/Mines Paristech publication
5. Breiman L, Friedman J, Stone CJ, Olshen RA. 1984. Classification and regression trees. London: Chapman and Hall/CRC
6. Brunel G, Borianne P, Subsol G, Jaeger M, Caraglio Y. 2012. Automatic characterization of the cell organization in light microscopic images of wood: Application to the identification of the cell file. In: Guo Y, Kang MZ, DumontY, eds. Plant growth modeling, simulation, visualization and applications. The Fourth International Symposium on Plant Growth Modeling, Simulation, Visualization and Applications, Shanghai, China, 58-65
7. Brunel G, Borianne P, Subsol G, Jaeger M. 2013. Simple-graphs fusion in image mosaic: application to automated cell files identification in wood slices. In: Kämäräinen J-K, Koskela M, eds. Lecture notes in computer science. Image analysis. Berlin: Springer, 34-43
8. Busin L, Shi J, Vandenbroucke N, Macaire L. 2009. Color space selection for color image segmentation by spectral clustering. IEEE International Conference on Signal and Image Processing Applications (ICSIPA), 262-267. http://ieeexplore.ieee.org/xpl/articleDetails.jsp?tp=&arnumber=5478603&queryText%3DColor+space+selection+for+color+image+segmentation+by+spectral+clustering
9. Clair B, Gril J, DiRenzo F, Yamamoto H, Quignard F. 2007. Characterization of a gel in the cell wall to elucidate the paradoxical shrinkage of tension wood. Biomacromolecules 9: 494-498
10. Cormen TH, Cazin X, Kocher G-L. 2001. AD. Introduction to algorithms. Cambridge, MA: MIT Press
11. Forgy E. 1965. Cluster analysis of multivariate data: efficiency vs interpretability of classifications. Biometrics 21: 768-769
12. Fourcaud T, Zhang X, Stokes A, Lambers H, Körner C. 2008. Plant growth modelling and applications: the increasing importance of plant architecture in growth models. Annals of Botany 101: 1053-1063
13. Gilmore P, Kelley C.T. 1995. An implicit filtering algorithm for optimization of functions with many local minima. SIAM Journal 5: 269-285
14. Gindl W. 2001. Cell-wall lignin content related to tracheid dimensions in drought-sensitive Austrian pine (Pinus nigra). Iawa Journal 22: 113-120
15. Guédon Y, Taugourdeau O, Caraglio Y, Sabatier S. 2013. Characterizing the balance between ontogeny and environmental constraints in forest tree development using growth phase duration distributions. In: Nygren P, Lintunen A, Godin C, Nikinmaa E, Sievänen R, eds. Proceedings of the 7th International Conference on Functional Structural Plant Models, Saariselka, Finland, 237-239
16. Haddad RA, Akansu AN. 1991. A class of fast Gaussian binomial filters for speech and image processing. IEEE Transactions on Signal Processing 39: 723-727
17. Heinrich L. 2007. Reaction wood varieties caused by different experimental treatments. TRACE, Tree Rings in Archaeology. Climatology and Ecology 5: 224-232
18. Hitz OM, Gärtner H, Heinrich I, Monbaron M. 2008. Wood anatomical changes in roots of European ash (Fraxinus excelsior L) after exposure. Dendrochronologia 25: 145-152
19. Jeacocke MB, Lovell BC. 1994.Amulti-resolutionalgorithmforcytological image segmentation. Proceedings of the 1994 Second Australian and New Zealand Conference on Intelligent Information Systems, 1994. IEEE, 322-326
20. Jones R, Bischof L. 1996. A graph-based segmentation of wood micrographs. Computing science and statistics 28. Proceedings in Computing Science and Statistics 28: 12-20
21. Kennel P, Subsol G, Guéroult M, Borianne P. 2010. Automatic identification of cell files in light microscopic images of conifer wood. In: Djemal K, Deriche M, eds. 2nd International Conference on Image Processing Theory, Tools and Applications. IEEE, 98-103
22. Krause C, Rossi S, Thibeault-Martel M, Plourde P-Y. 2010. Relationships of climate and cell features in stems and roots of black spruce and balsam fir. Annals of Forest Science 67: 402-402
23. Lachaud S, Catesson A-M, Bonnemain J-L. 1999. Structure and functions of the vascular cambium. Comptes Rendus de l'Académie des Sciences
24. Series III. Sciences de la Vie 322: 633-650
25. Ladjal S. 2006. Estimation du flou dans les images naturelles. In: Quinziéme congrés francophone Reconnaissance des formes et intelligence artificielle (RFIA). François-Rabelais University Press (in French
26. Marcuzzo M, Quelhas P, Campilho A, Mendonça AM, Campilho A. 2009. AutomatedArabidopsis plant root cell segmentation based on SVMclassification and region merging. Computers in Biology and Medicine 39: 785-793
27. McInerney T, Terzopoulos D. 1999. T-Snakes: topology adaptive snakes. Medical Image Analysis 4: 840-845
28. Moëll MK, Donaldson LA. 2001. Comparison of segmentation method for digital image analysis of confocal microscope images to measaure tracheid cell dimensions. IAWA Journal 22: 267-288
29. Nicolini E, Caraglio Y, Pélissier R, Leroy C, Roggy J. 2003.Epicormic branches: a growth indicator for the tropical forest tree, Dicorynia guianensis Amshoff (Caesalpiniaceae). Annals of Botany 92: 97-105
30. Park J, Keller JM. 2001. Snakes on the watershed. IEEE Transactions on Pattern Analysis and Machine Intelligence 23: 1201-1205
31. Quelhas P, Nieuwland J, Dewitte W, Mendonça AM, Murray J, Campilho A. 2011. Arabidopsis thaliana automatic cell file detection and cell length estimation. In: Kamel M, Campilho A, eds. Image analysis and recognition. Berlin: Springer, 1-11
32. Rathgeber CBK, Rossi S, Bontemps J-D. 2011. Cambial activity related to tree size in a mature silver-fir plantation. Annals of Botany 108: 429-438
33. Rossi S, Deslauriers A, Anfodillo T, et al. 2006. Conifers in cold environments synchronize maximum growth rate of tree-ring formation with day length. New Phytologist 170: 301-310
34. Rossi S, Simard S, Rathgeber CBK, Deslauriers A, Zan CD. 2009. Effects of a 20-day-long dry period on cambial and apical meristem growth in Abies balsamea seedlings. Trees 23: 85-93
35. Rozenberg P, Schü te G, Ivkovich M, Bastien C, Bastien J-C. 2004. Clonal variation of indirect cambium reaction to within-growing season temperature changes in Douglas-fir. Forestry 77: 257-268
36. Schneider CA, Rasbaand WS, Eliceiri KW. 2012. NIH Image to ImageJ: 25 years of image analysis. Nature Methods 9: 671-675
37. Thibeault-Martel M, Krause C, Morin H, Rossi S. 2008. Cambial activity and intra-Annual xylem formation in roots and stems of Abies balsamea and Picea mariana. Annals of Botany 102: 667-674
38. Travis AJ, Hirst DJ, Chesson A. 1996. Automatic classification of plant cells according to tissue type using anatomical features obtained by the distance transform. Annals of Botany 78: 325-331
39. Vincent L, Soille P. 1991. Watersheds in digital spaces: an efficient algorithm based on immersion simulations. IEEE Transactions on Pattern Analysis and Machine Intelligence 13: 583-598
40. Wang M, Zhou X, Li F, Huckins J, King RW, Wong STC. 2008. Novel cell segmentation and online SVM for cell cycle phase identification in automated microscopy. Bioinformatics 24: 94-101
41. Zunic J, Hirota K. 2008. Measuring shape circularity. Progress in Pattern Recognition, Image Analysis and Applications Lecture Notes. Computer Science 5197: 94-101