Mapping resinwood in CT scans of Scots pine with a hybrid Gaussian Mixture Model image segmentation pipeline

Although X‑ray computed tomography (CT) provides a non‑destructive means of visualising internal density, robust and automated methods to separate resinwood from heartwood and sapwood in such scans remain underdeveloped. To address this, we present a hybrid image analysis pipeline that combines a slice‑adaptive Gaussian Mixture Model (GMM), this statistically characterises tissue‑specific density patterns in each CT slice.

This dataset was created through X-ray computed tomography (CT) imaging for developing segmentation method that capable identifying internal resinwood. Infected pine tree samples were inspected and harvested by trained inventory specialist, then scanned by MicroTec Mito industrial-scale CT scanner. The scanner produced data using a cone beam and two angled flat detectors on a helical scanning trajectory.