Large-Scale Stratigraphic Analysis of Paintings by OCT: A Supervised Learning and Volumetric Stitching Approach

Non-invasive characterization of painting stratigraphy is challenged by light attenuation in optically heterogeneous opaque materials composing micrometric layers. Optical coherence tomography (OCT) provides suitable, non-invasive, depth-resolved imaging capabilities; however, large-area stratigraphic analysis is limited by the restricted field of view of individual stacks, high data dimensions, and the lack of robust methods for stitching volumes acquired at different focal depths. In addition, conventional layer thickness estimation relies on manual identification of intensity peaks along A-scans, which is time-consuming, operator-dependent, and unsuitable for large-scale analysis. Building upon our prior work, which introduced an AI-enhanced method for OCT volume analysis, we present an automated workflow integrating supervised semantic segmentation, volumetric mosaic stitching, and pixel-wise layer thickness quantification. OCT B-scans are segmented using a Random Forest classifier within the Trainable Weka Segmentation framework to delineate material interfaces. Adjacent OCT volumes are then combined into a continuous mosaic, and interfacial distances are computed using a custom MATLAB routine to obtain pixel-wise thickness measurements over extended fields of view. The model discriminates air/paint and paint/primer interfaces with an accuracy ranging from 96.4% to 97.1% and a weighted average F1-score exceeding 0.95, enabling quantitative reconstruction of paint thickness with micrometric resolution. This method enables efficient and reproducible analysis of large OCT datasets, extends stratigraphic characterization to macroscopic fields of view while maintaining micrometric resolution, and reduces processing time while improving consistency compared to manual approaches.