Wavelets filtering for classification of very noisy electron microscopic single particles images- application on structure determination of VP5-VP19C recombinant
Saad, Ali S. . 2003
Abstract
Background: Images of frozen hydrated [vitrified] virus particles were taken close-to-focus in an
electron microscope containing structural signals at high spatial frequencies. These images had very
low contrast due to the high levels of noise present in the image. The low contrast made particle
selection, classification and orientation determination very difficult. The final purpose of the
classification is to improve the signal-to-noise ratio of the particle representing the class, which is
usually the average. In this paper, the proposed method is based on wavelet filtering and multiresolution
processing for the classification and reconstruction of this very noisy data. A multivariate
statistical analysis (MSA) is used for this classification.
Results: The MSA classification method is noise dependant. A set of 2600 projections from a 3D
map of a herpes simplex virus -to which noise was added- was classified by MSA. The classification
shows the power of wavelet filtering in enhancing the quality of class averages (used in 3D
reconstruction) compared to Fourier band pass filtering. A 3D reconstruction of a recombinant
virus (VP5-VP19C) is presented as an application of multi-resolution processing for classification
and reconstruction.
Conclusion: The wavelet filtering and multi-resolution processing method proposed in this paper
offers a new way for processing very noisy images obtained from electron cryo-microscopes. The
multi-resolution and filtering improves the speed and accuracy of classification, which is vital for the
3D reconstruction of biological objects. The VP5-VP19C recombinant virus reconstruction
presented here is an example, which demonstrates the power of this method. Without this
processing, it is not possible to get the correct 3D map of this virus.
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