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International Union of Crystallography, Journal of Applied Crystallography, 1(41), p. 8-17, 2008

DOI: 10.1107/s0021889807049308

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Automated classification of crystallization experiments using wavelets and statistical texture characterization techniques

Journal article published in 2008 by D. Watts, K. Cowtan ORCID, J. Wilson
This paper was not found in any repository, but could be made available legally by the author.
This paper was not found in any repository, but could be made available legally by the author.

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Abstract

A method is presented for the classification of protein crystallization images based on image decomposition using the wavelet transform. The distribution of wavelet coefficient values in each sub-band image is modelled by a generalized Gaussian distribution to provide discriminatory variables. These statistical descriptors, together with second-order statistics obtained from joint probability distributions, are used with learning vector quantization to classify protein crystallization images.