Imperial College London
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ProfessorMartinBlunt

Faculty of Engineering, Department of Earth Science & Engineering

Chair in Flow in Porous Media
 
 
 
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Contact

 

+44 (0)20 7594 6500m.blunt Website

 
 
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Location

 

2.38ARoyal School of MinesSouth Kensington Campus

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Summary

 

Publications

Citation

BibTex format

@article{Lutz-Bueno:2018:10.1107/S1600576718011032,
author = {Lutz-Bueno, V and Arboleda, C and Leu, L and Blunt, MJ and Busch, A and Georgiadis, A and Bertier, P and Schmatz, J and Varga, Z and Villanueva-Perez, P and Wang, Z and Lebugle, M and David, C and Stampanoni, M and Diaz, A and Guizar-Sicairos, M and Menzel, A},
doi = {10.1107/S1600576718011032},
journal = {Journal of Applied Crystallography},
pages = {1378--1386},
title = {Model-free classification of X-ray scattering signals applied to image segmentation},
url = {http://dx.doi.org/10.1107/S1600576718011032},
volume = {51},
year = {2018}
}
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RIS format (EndNote, RefMan)

TY  - JOUR
AB  - In  most  cases,  the  analysis  of  small-angle  and  wide-angle  X-ray  scattering(SAXS  and  WAXS,  respectively)  requires  a theoretical  model  to  describe  thesample’s  scattering,  complicating  the  interpretation  of  the  scattering  resultingfrom  complex  heterogeneous  samples.  This  is  the  reason  why,  in  general,  theanalysis of a large number of scattering patterns, such as are generated by time-resolved   and   scanning   methods,   remains   challenging.   Here,   a   model-freeclassification  method  to  separate  SAXS/WAXS  signals  on  the  basis  of  theirinflection  points  is  introduced  and  demonstrated.  This  article  focuses  on  thesegmentation of scanning SAXS/WAXS maps for which each pixel correspondsto  an  azimuthally  integrated  scattering  curve.  In  such  a  way,  the  samplecomposition distribution can be segmented through signal classification withoutapplying a model or previous sample knowledge. Dimensionality reduction andclustering algorithms are employed to classify SAXS/WAXS signals according totheir similarity. The number of clusters,i.e.the main sample regions detected bySAXS/WAXS signal similarity, is automatically estimated. From each cluster, amain  representative  SAXS/WAXS  signal  is  extracted  to  uncover  the  spatialdistribution  of  the  mixtures  of  phases  that  form  the  sample.  As  examples  ofapplications, a mudrock sample and two breast tissue lesions are segmented.
AU  - Lutz-Bueno,V
AU  - Arboleda,C
AU  - Leu,L
AU  - Blunt,MJ
AU  - Busch,A
AU  - Georgiadis,A
AU  - Bertier,P
AU  - Schmatz,J
AU  - Varga,Z
AU  - Villanueva-Perez,P
AU  - Wang,Z
AU  - Lebugle,M
AU  - David,C
AU  - Stampanoni,M
AU  - Diaz,A
AU  - Guizar-Sicairos,M
AU  - Menzel,A
DO  - 10.1107/S1600576718011032
EP  - 1386
PY  - 2018///
SN  - 0021-8898
SP  - 1378
TI  - Model-free classification of X-ray scattering signals applied to image segmentation
T2  - Journal of Applied Crystallography
UR  - http://dx.doi.org/10.1107/S1600576718011032
UR  - http://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000445614800012&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=1ba7043ffcc86c417c072aa74d649202
UR  - http://hdl.handle.net/10044/1/63262
VL  - 51
ER  -
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