Automated 3D tree-ring detection and measurement from X-ray computed tomography

Tree ring analysis is essential to reveal the environmental information encoded in the wood structure. It provides quantitative data on the anatomical structure which can be used, for example, to measure the impact of the fluctuating environment on the tree growth, to support global vegetation models and for the dendrochronological analysis of archaeological wooden artefacts. Currently, several imaging-based methods for tree-ring detection and tree-ring feature estimation exist. However, despite advances in computer vision and edge recognition algorithms, detection of tree-rings is mostly limited to two-dimensional (2D) datasets and performed manually in some cases. This paper describes a new approach to estimate the three-dimensional (3D) structure of tree rings and their width automatically from X-ray computed tomography data. This approach relies on a modified Canny edge detection algorithm, which is capable of detecting fully connected tree-ring edges throughout the image stack. Our results show that this approach performs well on six tree species having conifer, ring-porous and diffuse-porous ring boundary structures. In our study, image denoising proved to be a critical step to achieve accurate results. A major advantage of this procedure is that it requires very little to no user interaction rendering it a reproducible procedure for tree-ring width measurements. As it also provides 3D representations of the ring edges, it also may be used in the future for the inspection of anatomical features.