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Classification of protein crystallization images using Fourier descriptors
Author(s) -
Foadi James,
Walker Christopher G.,
Wilson Julie
Publication year - 2007
Publication title -
journal of applied crystallography
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.429
H-Index - 162
ISSN - 1600-5767
DOI - 10.1107/s0021889807011156
Subject(s) - fourier transform , spatial frequency , frequency domain , power law , artificial intelligence , pattern recognition (psychology) , polar coordinate system , mathematics , range (aeronautics) , computer science , optics , mathematical analysis , physics , materials science , statistics , geometry , composite material
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 modelled by an equation of power law form, I = Ar α , where A and α 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 α, 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 crystallization drop to obtain an improved overall classification rate.