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Automated classification of crystallisation experiments using wavelets and statistical texture characterisation techniques

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Automated classification of crystallisation experiments using wavelets and statistical texture characterisation techniques. / Watts, David; Cowtan, Kevin; Wilson, Julie C.

In: Journal of Applied Crystallography, Vol. 41, No. 1, 01.04.2008, p. 8-17.

Research output: Contribution to journalArticlepeer-review

Harvard

Watts, D, Cowtan, K & Wilson, JC 2008, 'Automated classification of crystallisation experiments using wavelets and statistical texture characterisation techniques', Journal of Applied Crystallography, vol. 41, no. 1, pp. 8-17. https://doi.org/10.1107/S0021889807049308

APA

Watts, D., Cowtan, K., & Wilson, J. C. (2008). Automated classification of crystallisation experiments using wavelets and statistical texture characterisation techniques. Journal of Applied Crystallography, 41(1), 8-17. https://doi.org/10.1107/S0021889807049308

Vancouver

Watts D, Cowtan K, Wilson JC. Automated classification of crystallisation experiments using wavelets and statistical texture characterisation techniques. Journal of Applied Crystallography. 2008 Apr 1;41(1):8-17. https://doi.org/10.1107/S0021889807049308

Author

Watts, David ; Cowtan, Kevin ; Wilson, Julie C. / Automated classification of crystallisation experiments using wavelets and statistical texture characterisation techniques. In: Journal of Applied Crystallography. 2008 ; Vol. 41, No. 1. pp. 8-17.

Bibtex - Download

@article{da4b6f696d3b4f0f9d01cfae6dee3980,
title = "Automated classification of crystallisation experiments using wavelets and statistical texture characterisation techniques",
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.",
keywords = "classification;, pattern recognition;, crystallization images;, wavelet transform;, statistical descriptors",
author = "David Watts and Kevin Cowtan and Wilson, {Julie C.}",
year = "2008",
month = apr,
day = "1",
doi = "10.1107/S0021889807049308",
language = "English",
volume = "41",
pages = "8--17",
journal = "Journal of Applied Crystallography",
issn = "0021-8898",
publisher = "International Union of Crystallography",
number = "1",

}

RIS (suitable for import to EndNote) - Download

TY - JOUR

T1 - Automated classification of crystallisation experiments using wavelets and statistical texture characterisation techniques

AU - Watts, David

AU - Cowtan, Kevin

AU - Wilson, Julie C.

PY - 2008/4/1

Y1 - 2008/4/1

N2 - 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.

AB - 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.

KW - classification;

KW - pattern recognition;

KW - crystallization images;

KW - wavelet transform;

KW - statistical descriptors

UR - http://www.scopus.com/inward/record.url?scp=38349177308&partnerID=8YFLogxK

U2 - 10.1107/S0021889807049308

DO - 10.1107/S0021889807049308

M3 - Article

VL - 41

SP - 8

EP - 17

JO - Journal of Applied Crystallography

JF - Journal of Applied Crystallography

SN - 0021-8898

IS - 1

ER -