Lutz-Bueno V, Arboleda C, Leu L, Blunt MJ, 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. Model-free classification of X-ray scattering signals applied to image segmentation.
J Appl Crystallogr 2018;
51:1378-1386. [PMID:
30279640 PMCID:
PMC6157705 DOI:
10.1107/s1600576718011032]
[Citation(s) in RCA: 7] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/06/2018] [Accepted: 08/02/2018] [Indexed: 11/17/2022] Open
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.
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.
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