In most cases, the analysis of small-angle and wide-angle X-ray scattering
(SAXS and WAXS, respectively) requires a theoretical model to describe the
sample’s scattering, complicating the interpretation of the scattering resulting
from complex heterogeneous samples. This is the reason why, in general, the
analysis of a large number of scattering patterns, such as are generated by time-
resolved and scanning methods, remains challenging. Here, a model-free
classification method to separate SAXS/WAXS signals on the basis of their
inflection points is introduced and demonstrated. This article focuses on the
segmentation of scanning SAXS/WAXS maps for which each pixel corresponds
to an azimuthally integrated scattering curve. In such a way, the sample
composition distribution can be segmented through signal classification without
applying a model or previous sample knowledge. Dimensionality reduction and
clustering algorithms are employed to classify SAXS/WAXS signals according to
their similarity. The number of clusters,
i.e.
the main sample regions detected by
SAXS/WAXS signal similarity, is automatically estimated. From each cluster, a
main representative SAXS/WAXS signal is extracted to uncover the spatial
distribution of the mixtures of phases that form the sample. As examples of
applications, a mudrock sample and two breast tissue lesions are segmented.