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Image Processing Approach for Detection of Three-Dimensional Tree-Rings from X-Ray Computed Tomography

Authors: Jorge Martinez-Garcia, Ingrid Stelzner, Joerg Stelzner, Damian Gwerder, Philipp Schuetz

Abstract:

Tree-ring analysis is an important part of the quality assessment and the dating of (archaeological) wood samples. It provides quantitative data about the whole anatomical ring structure, which can be used, for example, to measure the impact of the fluctuating environment on the tree growth, for the dendrochronological analysis of archaeological wooden artefacts and to estimate the wood mechanical properties. Despite advances in computer vision and edge recognition algorithms, detection and counting of annual rings are still limited to 2D datasets and performed in most cases manually, which is a time consuming, tedious task and depends strongly on the operator’s experience. This work presents an image processing approach to detect the whole 3D tree-ring structure directly from X-ray computed tomography imaging data. The approach relies on a modified Canny edge detection algorithm, which captures fully connected tree-ring edges throughout the measured image stack and is validated on X-ray computed tomography data taken from six wood species.

Keywords: Ring recognition, edge detection, X-ray computed tomography, dendrochronology.

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References:


[1] Dietz, H. & Von Arx, G. (2005), ‘Climatic fluctuations causes large-scale synchronous variation in radial root increments of perennial forbs.’, Ecology 86, 327–333.
[2] Gedalof, Z. & Berg, A. A (2010), ‘Tree ring evidence for limited direct CO2 fertilization of forests over the 20th century.’ Global Biogeochemical Cycles 24, GB3027.
[3] Cufar, K., Tegel, W., Merela, M., Kromer, B. & Velus ̆c ̆ek, A. (2015), ‘Ene- olithic pile dwellings south of the alps precisely dated with tree-ring chronologies from the north.’, Dendrochronologia 35, 91–98.
[4] Kharrat, W., Koubaa, A., Khlif, M. & Bradai, C. (2019), ‘Intra-ring wood density and dynamic modulus of elasticity profiles for black spruce and jack pine from X-ray densitometry and ultrasonic wave velocity measurement.’, Forest 10, 569.
[5] Speer, J. H (2010), Fundamentals of Tree-ring Research., University of Arizona Press.
[6] Conner, W. S., Schowengerdt, R. A., Munro, M. & Hughes, M. K. (1998), Design of a computer vision based tree ring dating system., in ‘IEEE Southwest Symposium on Image Analysis and Interpretation’, pp. 256– 261.
[7] Laggoune, H., Sarifuddin & Guesdon, V. (2005), Tree ring analysis., in ‘Canadian Conference on Electrical and Computer Engineering’, pp. 1574–1577.
[8] Wang, H.-J., Qi,Heng-nian, Zhang, Guang-qun, Li, Wen-zhu & Wang, Bi-hui (2010), An automatic method of tree-rings boundary detection on wood micro-images., in ‘2010 International Conference on Computer Application and System Modeling’, pp. V2–477–V2–480.
[9] Fabijańska, A., Danek, M., Barniak, J. & Piórkowski, A. (2017), ‘Towards automatic tree rings detection in images of scanned wood samples.’, Computers and Electronics in Agriculture 140, 279–289.
[10] Fabijańska, A. & Danek, M. (2018), ‘Deepdendro – A tree rings detector based on a deep convolutional neural network.’, Computers and Electronics in Agriculture 150, 353–363.
[11] J. Martinez-Garcia, Ingrid Stelzner, Joerg Stelzner, Damian Gwerder and Philipp Schuetz (2021), ‘Automated 3D tree-ring detection and measurements from X-ray computed tomography’ Dendrochronologia, (submitted).
[12] Canny, J. (1986), ‘A computational approach to edge detection.’, IEEE Transactions on Pattern Analysis and Machine Intelligence 8, 679–698.
[13] Buades, A., Coll, B. & Morel, J. M. (2011), ‘Non-local means denoising.’, Image Processing On Line 1, 208–212.
[14] Yang, G.J. & Huang, T. S. (1981), ‘The effect of median filtering on edge lo- cation estimation.’, Computer Graphics and Image Processing 15, 224-245.
[15] Otsu, N. (1979), ‘A threshold selection method from gray-level histograms.’, IEEE Transactions on Systems, Man, and Cybernetics 9, 62– 66.
[16] Lignum, Holzwirtschaft Schweiz (2020), www.lignum.ch.
[17] S. Carmignato, W. Dewulf, R. Leach, Industrial X-Ray Computed Tomography, Springer, Cham, 2018.
[18] J. Van den Bulcke, E. L. Wernersson, m. Dierick, D. Van Loo, B. Masschaele, L. Brabant, M. N. Boone, L. Van Hoorebeke, K. Haneca, A. Brun, C. L. Hendriks, J. Van Acker, 3d tree-ring anal- ysis using helical x-ray tomography, Dendrochronologia 32 (2014) 39–46.
[19] A. Vannoppen, P. Boeckx, T. DeMil, V. Kint, Q. Ponette, J., J. Vanden Bulcke, K. Verheyen, B. Muys, Climate driven trends in tree biomass increment show asynchronous dependence on tree-ring width and wood density variation, Dendrochronologia 48 (2018) 40–51.
[20] T. De Mil, A. Vannoppen, H. Beeckman, J. Van Acker, J. Van den Bulcke, A field-to-desktop toolchain for x-ray ct densitometry enables tree ring analysis, Annals of Botany 117 (2016) 1187–1196.