A hybrid image segmentation framework for resin wood detection in Scots pine X-ray computed tomography scans using adaptive Gaussian mixture modelling

Extensive resin wood formation in Scots pine stems infected by Cronartium pini constitutes a significant quality defect that compromises timber value and complicates industrial processing decisions. While X-ray computed tomography (CT) offers non-destructive density mapping capabilities, the wood processing industry currently lacks validated, automated segmentation methods to differentiate resin wood from unaffected heartwood and sapwood. A hybrid automated segmentation pipeline incorporating an adaptive per-slice Gaussian mixture model (GMM) was developed to combine statistical parameterisation of tissue-specific intensity distributions with multi-directional ray-casting for structural likelihood scoring. The framework utilises unsupervised image processing methodologies and morphological refinement to improve boundary delineation between complex internal wood features. Validation against manual annotations by trained technicians demonstrated high precision (0.94) for sapwood segmentation and substantial inter-rater agreement (Cohen’s κ = 0.819). However, precision for resin wood was limited to 0.27, reflecting the inherent difficulty of distinguishing pathological resin saturation from natural impregnation of heartwood. By implementing heatmap-based logic to overcome the limitations of simple intensity thresholding, this research establishes a foundational open-source pathway for automated internal resin wood mapping. The proposed pipeline offers a computationally efficient approach for research-scale analysis and provides a viable strategy towards potential value recovery in sawmills.