Automatic segmentation of acoustic tomography images for the measurement of wood decay

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.