Recognition of Viruses by Electron Microscopy using Higher order Spectral Features

摘要

A limitation of using electron microscopy as a diagnostic tool in virology is the expertise required in analysing and interpreting the images. EM images of different viruses can be very similar in shape. An automated recognition method is proposed in this paper. It is based on radial spectra of higher-order spectral parameters robust to translation, scaling and noise. These features are also rotation invariant and can be averaged for a population of viral particles without the need to normalize and align them. They extract symmetry information and are sensitive enough to distinguish viruses that appear nearly circular to the human eye. The method was tested using three such viruses with very similar morphologies - the Adeno, the HAV and the Astro. 70 viral particles of each class from three images were used for training. In the first test, random unseen sets of viral particles from the same images were chosen. In the second test, images of viruses from other sources, where the specimen preparation and the microscope are different, were used to determine the reliability of the system. Both tests have shown high classification accuracy improving rapidly to 100% as the test ensemble grew to 20 particles.