Rule-driven defect detection in CT images of hardwood logs

This paper deals with automated detection and identification of internal defects in hardwood logs using computed tomography (CT) images. We have developed a system that employs artificial neural networks (ANNs) to perform tentative classification of logs on a pixel-by-pixel basis. This approach achieves a high level of classification accuracy for several hardwood species (northern red oak, Quercus rubra, L., water oak, Q. nigra, L., yellow poplar, Liriodendron tulipifera, L., and black cherry, Prunus serotina, Ehrh.), and three common defect types (knots, splits, and decay). Although the results are very satisfactory statistically, a subjective examination reveals situations that could be refined in a subsequent post-processing step. We are currently developing a rule-based, contextual approach to region refinement that augments the initial emphasis on local information. The resulting rules are domain dependent, utilizing information that depends on region shape and type of defect. For example splits tend to be long and narrow, and this knowledge can be used to merge smaller, disjoint regions that have tentatively been labeled as splits. Similarly, image regions that represent knots, decay, and clear wood can be refined by removing small, spurious regions and by smoothing the boundaries of these regions. Mathematical morphology operators can be used for most of these tasks. This paper provides details concerning the domain-dependent rules by which morphology operators are chosen, and for merging results from different operations.