PithExtract: A robust algorithm for pith detection in computer tomography images of wood – Application to 125 logs from 17 tree species

Abstract

An algorithm to automatically detect the stem pith within X-ray scanned logs was adapted and validated for a wide range of tree species on the basis of an initial version developed for Picea abies by Longuetaud et al. (2004). The algorithm was enhanced by using adaptive thresholds, a final smoothing operation and an optional reversion of the CT slice order for better accuracy in the presence of branch forks. The 3D aspect of CT slice stacks was used both to reduce the processing time and to correct the pith position on some CT slices containing knots. The current improved version of the algorithm was published under the GPL and implemented as a plug-in for ImageJ software. It was validated on a big sample covering a very wide range of tree species. A total of 125 logs of 17 species (mainly hardwood) were tested (in total, 100451 images were processed). The results of pith detection were accurate for most of the logs, regardless of their position within the tree. The overall mean error was 1.69mm. The highest errors (above 10mm) were observed for five logs of Sorbus torminalis, Carpinus betulus and Acer campestre due to narrow annual ring widths with respect to the pixel size or to a low contrast in the CT images. The potential applications of the method under industrial conditions are discussed.