Model‐free classification of X‐ray scattering signals applied to image segmentation. Issue 5 (10th September 2018)

• Record Type: Journal Article
• Authors: Lutz-Bueno, V.; Arboleda, C.; Leu, L.; Blunt, M. J.; Busch, A.; Georgiadis, A.; Bertier, P.; Schmatz, J.; Varga, Z.; Villanueva-Perez, P.; Wang, Z.; Lebugle, M.; David, C.; Stampanoni, M.; Diaz, A.; Guizar-Sicairos, M.; Menzel, A.
• Is Part Of: Journal of applied crystallography. Volume 51:Issue 5(2018)
• Page Start: 1378
• Page End: 1386
• Publication Date: 2018-09-10
• Subjects: polarized resonant soft X‐ray scattering -- anisotropic nanostructures -- electromagnetic modeling; Crystallography -- Periodicals; 548.05
• DOI: 10.1107/S1600576718011032
• Languages: English
• ISSNs: 0021-8898

Abstract

Abstract : This article describes a modeling framework to relate the molecular orientation of nanostructures to polarized resonant soft X‐ray scattering measurements using the Born approximation and a full tensor treatment. Abstract : 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.