Xrayanalyse

Xrayanalyse is a modular computational framework for analyzing X-ray imaging data. It provides tools for preprocessing, feature extraction, quantitative analysis, and visualization to support interpretation of X-ray observations across scientific and industrial domains. The framework aims to unify disparate analysis methods under a common workflow and promote reproducibility by encapsulating processing steps and parameters.

Origin and naming: Xrayanalyse originated in a collaboration of researchers from several universities and imaging centers in the 2010s. The name combines 'X-ray' with 'analyse' to reflect its focus on analyzing X-ray data. It has since evolved through community contributions and standardized data interfaces.

Core methodology: The pipeline typically includes data ingestion from formats such as DICOM, TIFF or HDF5, preprocessing for noise reduction and artifact correction, segmentation and feature extraction, and statistical modeling. It supports Bayesian inference, machine learning-based classification, and multi-scale analysis, often enabling phase mapping, porosity quantification, crack detection, and mineralogical inference using diffraction and tomography data.

Applications: In materials science, Xrayanalyse is used to characterize microstructure, porosity, and stress fields in metals and ceramics. In medical imaging and radiology, it supports quantitative assessments like bone mineral density and trabecular architecture. In cultural heritage, it aids non-destructive analysis of artifacts. The toolkit is also used in quality assurance for manufacturing and nondestructive testing.

Availability and impact: The project is distributed as open-source software with a modular API. It provides reference implementations, documentation, and example datasets to facilitate reproducibility. While praised for flexibility and transparency, practitioners note that effective use requires domain-specific calibration and careful validation against ground truth.