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Classification of protein crystallisation images using Fourier descriptors.

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Classification of protein crystallisation images using Fourier descriptors. / Walker, Christopher G.; Foadi, James; Wilson, Julie.

In: Journal of Applied Crystallography, Vol. 40, No. 3, 06.2007, p. 418-426.

Research output: Contribution to journalArticlepeer-review

Harvard

Walker, CG, Foadi, J & Wilson, J 2007, 'Classification of protein crystallisation images using Fourier descriptors.', Journal of Applied Crystallography, vol. 40, no. 3, pp. 418-426. https://doi.org/10.1107/S0021889807011156

APA

Walker, C. G., Foadi, J., & Wilson, J. (2007). Classification of protein crystallisation images using Fourier descriptors. Journal of Applied Crystallography, 40(3), 418-426. https://doi.org/10.1107/S0021889807011156

Vancouver

Walker CG, Foadi J, Wilson J. Classification of protein crystallisation images using Fourier descriptors. Journal of Applied Crystallography. 2007 Jun;40(3):418-426. https://doi.org/10.1107/S0021889807011156

Author

Walker, Christopher G. ; Foadi, James ; Wilson, Julie. / Classification of protein crystallisation images using Fourier descriptors. In: Journal of Applied Crystallography. 2007 ; Vol. 40, No. 3. pp. 418-426.

Bibtex - Download

@article{52cad6415a9846f08892a07c9afb791c,
title = "Classification of protein crystallisation images using Fourier descriptors.",
abstract = "The two-dimensional Fourier Transform (2D-FT) is well suited to the extraction of features to differentiate image texture and the classification of images based on information acquired from the frequency domain provides a complementary method to approaches based within the spatial domain. The intensity, I, of the Fourier transformed images can be modeled by an equation of power law form, I = Ar?, where A and ? are constants and r is the radial spatial frequency. The power law is fitted over annuli, centred at zero spatial frequency, and the parameters, A and ?, determined for each spatial frequency range. The variation of the fitted parameters across wedges of fixed polar angle provides a measure of directionality and the deviation from the fitted model can be exploited for classification. The classification results are combined with an existing method to classify individual objects within the crystallisation drop to obtain an improved overall classification rate.",
keywords = "Classification, Crystallization images, Fourier transform, Pattern recognition",
author = "Walker, {Christopher G.} and James Foadi and Julie Wilson",
year = "2007",
month = jun,
doi = "10.1107/S0021889807011156",
language = "English",
volume = "40",
pages = "418--426",
journal = "Journal of Applied Crystallography",
issn = "0021-8898",
publisher = "International Union of Crystallography",
number = "3",

}

RIS (suitable for import to EndNote) - Download

TY - JOUR

T1 - Classification of protein crystallisation images using Fourier descriptors.

AU - Walker, Christopher G.

AU - Foadi, James

AU - Wilson, Julie

PY - 2007/6

Y1 - 2007/6

N2 - The two-dimensional Fourier Transform (2D-FT) is well suited to the extraction of features to differentiate image texture and the classification of images based on information acquired from the frequency domain provides a complementary method to approaches based within the spatial domain. The intensity, I, of the Fourier transformed images can be modeled by an equation of power law form, I = Ar?, where A and ? are constants and r is the radial spatial frequency. The power law is fitted over annuli, centred at zero spatial frequency, and the parameters, A and ?, determined for each spatial frequency range. The variation of the fitted parameters across wedges of fixed polar angle provides a measure of directionality and the deviation from the fitted model can be exploited for classification. The classification results are combined with an existing method to classify individual objects within the crystallisation drop to obtain an improved overall classification rate.

AB - The two-dimensional Fourier Transform (2D-FT) is well suited to the extraction of features to differentiate image texture and the classification of images based on information acquired from the frequency domain provides a complementary method to approaches based within the spatial domain. The intensity, I, of the Fourier transformed images can be modeled by an equation of power law form, I = Ar?, where A and ? are constants and r is the radial spatial frequency. The power law is fitted over annuli, centred at zero spatial frequency, and the parameters, A and ?, determined for each spatial frequency range. The variation of the fitted parameters across wedges of fixed polar angle provides a measure of directionality and the deviation from the fitted model can be exploited for classification. The classification results are combined with an existing method to classify individual objects within the crystallisation drop to obtain an improved overall classification rate.

KW - Classification

KW - Crystallization images

KW - Fourier transform

KW - Pattern recognition

U2 - 10.1107/S0021889807011156

DO - 10.1107/S0021889807011156

M3 - Article

AN - SCOPUS:34249077238

VL - 40

SP - 418

EP - 426

JO - Journal of Applied Crystallography

JF - Journal of Applied Crystallography

SN - 0021-8898

IS - 3

ER -