& Key message Considering anisotropy in image reconstruction algorithm for ultrasound computed tomography of trees resulted in a more accurate detection of defects compared to common approaches used. & Context Ultrasound computed tomography is a suitable tool for nondestructive evaluation of standing trees. Until now, to simplify the image reconstruction process, the transverse cross-section of trees has been considered as quasi-isotropic and therefore limiting the defect identification capability. & Aims An approach to solve the inverse problem for tree imaging is presented, using an ultrasound-based method (travel-time computed tomography) suited to the anisotropy of wood material and validated experimentally. & Methods The proposed iterative method focused on finding a polynomial approximation of the slowness in each pixel of the image depending on the angle of propagation, modifying the curved trajectories by means of a raytracing method. This method allowed a mapping of specific elastic constants using nonlinear regression. Experimental validation was performed using sections of green wood from a pine tree (Pinus pinea L.), with configurations that include a healthy case, a centered, and an off-centered defect. & Results Images obtained using the proposed method led to a more accurate location of the defects compared to the filtered backprojection algorithm (isotropic hypothesis), considered as reference. & Conclusion The performed experiments demonstrated that considering the wood anisotropy in the imaging process led to a better defect detection compared to the use of a common imaging technique.
In the assessment of standing trees, an acoustic tomographic device is a valuable tool as it permits to acquire data from the inner part of the trees without causing them to fall down unnecessarily. The interpretation of the images produced by these devices is part of the diagnosis process for urban trees management. This paper presents a segmentation methodology to identify defective regions in cross-section tomographic images obtained with an Arbotom® device. Two trunk samples obtained from a Blackwood Acacia tree (Acacia melanoxylon) were tested, simulating defects by drilling holes with known geometry, size and position and using different numbers of sensors. Tomograms from the trunk cross sections were processed to align the propagation velocity data with the corresponding region, either healthy or defective. The segmentation methodology proposed aims to find a velocity threshold value to separate the defective region adjusting a logistic regression model to obtain the value that maximizes a performance criterion, using in this case the geometric mean. Two criteria were used to validate this methodology: the geometric mean and the surface ratio detected. Although an optimal threshold value was found for each experiment, this value was strongly influenced by the defect characteristics and the number of sensors. The correctly segmented area ranging from 54 to 93% demonstrates that the threshold method is not always the most proper way to process this type of images, and thereby further research is required in image processing and analysis. (Résumé d'auteur
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