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Correlation‐based methods of automatic particle detection in electron microscopy images with smoothing by anisotropic diffusion
Author(s) -
Nicholson W. V.,
Malladi R.
Publication year - 2004
Publication title -
journal of microscopy
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.569
H-Index - 111
eISSN - 1365-2818
pISSN - 0022-2720
DOI - 10.1111/j.1365-2818.2004.01286.x
Subject(s) - anisotropic diffusion , smoothing , anisotropy , particle (ecology) , diffusion , noise (video) , preprocessor , correlation coefficient , correlation function (quantum field theory) , correlation , cross correlation , physics , artificial intelligence , optics , mathematics , computer vision , computer science , image (mathematics) , mathematical analysis , geometry , statistics , oceanography , optoelectronics , dielectric , thermodynamics , geology
Summary Two methods of correlation‐based automatic particle detection in electron microscopy images are compared – computing a cross‐correlation function or a local correlation coefficient vs. azimuthally averaged reference projections (either from a model or from experimental particle images). The ability of smoothing images by anisotropic diffusion to improve the performance of particle detection is also considered. Anisotropic diffusion is an effective method of preprocessing that enhances the edges and overall shape of particles while reducing noise. It is found that anisotropic diffusion improves particle detection by a local correlation coefficient when projections from a high‐resolution reconstruction are used as references. When references from experimental particle images are used, a cross‐correlation function shows a more marked improvement in particle detection in images smoothed by anisotropic diffusion.