Trees play a major ecological and sanitary role in modern cities. Nondestructive imaging methods allow to analyze the inner structures of trees, without altering their condition. In this study, we are interested on evaluating the influence of anisotropy condition in wood on the tomography image reconstruction using ultrasonic waves, by time-of-flight (TOF) estimation using the raytracing approach, a technique used particularly in the field of exploration seismography to simulate wave fronts in elastic media. Mechanical parameters from six wood species and one isotropic material were defined and their wave fronts and corresponding TOF values were obtained, using the proposed raytracing method. If the material presented anisotropy, the ray paths between the emitter and the receivers were not straight; therefore, curved rays were obtained for wood and the TOF measurements were affected. To obtain the tomographic image from the TOF measurements, the filtered back-projection algorithm was applied, a widely used technique in applications of straight ray tomography, but also commonly used in wood acoustic tomography. First, discs without inner defects for isotropic and wood materials (Spruce sample) were tested. Isotropic material resulted in a flat color image; for wood material, a gradient of velocities was obtained. After, centric and eccentric defects were tested, both for isotropic and orthotropic cases. From the results obtained for wood, when using a reconstruction algorithm intended for straight ray tomography, the images presented velocity variations from the border to the center that made difficult the discrimination of possible defects inside the samples, especially for eccentric cases.
The acoustic tomographic technique is used in the diagnosis process of standing trees. This paper presents a segmentation methodology to separate defective regions in cross-section tomographic images obtained with Arbotom® device. A set of experiments was proposed using two trunk samples obtained from a eucalyptus tree, simulating defects by drilling holes with known geometry, size and position and using different number of sensors. Also, tomographic images from trees presenting real defects were studied, by testing two different species with significant internal decay. Tomographic images and photographs from the trunk cross-section were processed to align the propagation velocity data with a corresponding region, healthy or defective. The segmentation was performed by finding a velocity threshold value to separate the defective region; a logistic regression model was fitted to obtain the value that maximizes a performance criterion, being selected the geometric mean. Accuracy segmentation values increased as the number of sensors augmented; also the position influenced the result, obtaining improved results in the case of centric defects.
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