Orthotropic alignment of 3D images refers to setting a volume’s orientation according to its orthotropic properties. It is particularly important in industrial computed tomography (CT), where alignment enables standardized orientation of similar objects, facilitating registration, including CAD-model registration, for precise defect measurement. Orthotropic alignment is critical in applications such as printed circuit board (PCB) inspection and analysis of complex components, e.g., measuring engine blade clearances. In the latter case, slices must align with specific anisotropic axes, which may not fully correspond to orthotropic properties, representing another practical scenario. Existing orthotropic alignment methods, however, suffer from limited accuracy and applicability. In this paper, we formalize the task and propose a robust, efficient, reproducible, fully automatic approach for aligning various volumes. Our method leverages a 3D fast Hough transform (FHT) to compute sums across a set of planes, enabling reliable detection of anisotropic axes and orthotropic features. The FHT has been successfully applied in 2D image processing and document recognition; here, extended to three dimensions, it serves as an effective tool for orthotropic alignment. While a naive implementation would require O(N5) operations, the 3D FHT reduces this to O(N3 log N), making it comparable with classical tensor-based approaches. We provide the implementation, evaluation, and assessment of the algorithm on both real and synthetic CT data. The results outperform baseline methods in both quality and robustness, while offering broader applicability and fewer optimization parameters. Potential applications in related fields are also discussed.
Kulagin et al. (Tue,) studied this question.