We introduce a pointwise multiscale texture operator providing a mathematically grounded and interpretable description of how local image context evolves across observation scales. The operator is defined through the scale evolution of a contextual multiscale signature derived from Gaussian scale-space representations, thereby establishing direct connections with diffusion processes and multiresolution analysis. This formulation yields a stable descriptor of texture transitions that is robust to moderate noise, blurring, and acquisition variability, while remaining independent of any specific segmentation strategy. As a demonstration of its practical utility, we embed the proposed operator within a texture-coherence-driven region-growing framework for image segmentation. The resulting algorithm is dimension independent, computationally tractable, and does not rely on training data or complex architectural design. We illustrate its performance on 3D cone-beam computed tomography (CBCT) datasets, where it enables coherent segmentation and volumetric reconstruction of the dental pulp chamber under clinically relevant conditions involving low contrast, partial volume effects, and heterogeneous textures. Beyond this specific application, the proposed operator constitutes a general framework for multiscale texture analysis and structural characterization, particularly suited to imaging scenarios where interpretability, robustness to acquisition degradation, and limited annotated data are critical.

Structural health in civil engineering involved maintaining a structure's integrity and performance over time, resisting loads and environmental effects. Ensuring long-term functionality was vital to prevent accidents, economic losses, and service interruptions. Structural health monitoring (SHM) systems used sensors to detect damage indicators such as vibrations and cracks, which were crucial for predicting service life and planning maintenance. Machine learning (ML) enhanced SHM by analyzing sensor data to identify damage patterns often missed by human analysts. ML models captured complex relationships in data, leading to accurate predictions and early issue detection. This research aimed to develop a methodology for training an artificial intelligence (AI) system to predict the effects of retrofitting on civil structures, using data from the KW51 bridge (Leuven). Dimensionality reduction with the Welch transform identified the first seven modal frequencies as key predictors. Unsupervised principal component analysis (PCA) projections and a K-means algorithm achieved $70\mathrm{\%}$ accuracy in differentiating data before and after retrofitting. A random forest algorithm achieved $99.19\mathrm{\%}$ median accuracy with a nearly perfect receiver operating characteristic (ROC) curve. The final model, tested on the entire dataset, achieved $99.77\mathrm{\%}$ accuracy, demonstrating its effectiveness in predicting retrofitting effects for other civil structures.