Automated analysis of CT images for the inspection of hardwood logs

This paper investigates several classifiers for labeling internal features of hardwood logs using computed tomography (CT) images. A primary motivation is to locate and classify internal defects so that an optimal cutting strategy can be chosen, Previous work has relied on combinations of low-level processing, image segmentation, autoregressive texture modeling, and knowledge-based analysis, Most previous work has also been limited to two-dimensional analysis of a single species only, This paper describes these approaches briefly, and compares them with a feed-forward neural-net classifier that we have developed, In order to accommodate species with different cell anatomies, CT density values are first normalized, Features are then extracted, primarily using local three-dimensional data, Somewhat surprisingly, this locality approach has resulted in a pixel-by-pixel classification accuracy of 95%. This accuracy improves during subsequent morphological processing steps which refine the detected defect regions in the images