Identification of gastroenteric viruses by electron microscopy using higher order spectral features.
BACKGROUND
Many paediatric illnesses are caused by viral agents, for example, acute gastroenteritis. Electron microscopy can provide images of viral particles and can be used to identify the agents.


OBJECTIVE
The use of electron microscopy as a diagnostic tool is limited by the need for high level of expertise in interpreting these images and the time required. A semi-automated method is proposed in this paper.


METHODS
The method is based on bispectal features that capture contour and texture information while providing robustness to shift, rotation, changes in size and noise. The magnification or true size of the viral particles need not be known precisely, but if available can be used additionally for improved classification. Viral particles from one or more images are segmented and analyzed to verify whether they belong to a particular class (such as Adenovirus, Rotavirus, etc.) or not. Two experiments were conducted-depending on the populations from which virus particle images were collected for training and testing, respectively. In the first, disjoint subsets from a pooled population of virus particles obtained from several images were used. In the second, separate populations from separate images were used. The performance of the method on viruses of similar size was separately evaluated using Astrovirus, HAV and Poliovirus. A Gaussian Mixture Model was used for the probability density of the features. A threshold on the log-likelihood is varied to study false alarm and false rejection trade-off. Features from many particles and/or likelihoods from independent tests are averaged to yield better performance.


RESULTS
An equal error rate (EER) of 2% is obtained for verification of Rotavirus (tested against three other viruses) when features from 15 viral particle images are averaged. It drops further to less than 0.2% when scores from two tests are averaged to make a decision. For verification of Astrovirus (tested against two others of the same size) the EER was less than 2% when 20 particles and two tests were used.


CONCLUSIONS
Bispectral features and Gaussian mixture modelling of their probability density are shown to be effective in identifying viruses from electron microscope images. With the use of digital imaging in electron microscopes, this method can be fully automated.
Ong
Hannah
H
Speech, Audio, Image and Video Technology Research Program, Queensland University of Technology, Brisbane, Qld 4001, Australia. cl.ong@student.qut.edu.au
Chandran
Vinod
V
D000260:Adenoviruses, Human / Q000648:ultrastructure; D002139:Caliciviridae / Q000648:ultrastructure; D005759:Gastroenteritis / Q000821:virology*; D006801:Humans; D007091:Image Processing, Computer-Assisted; D001257:Mamastrovirus / Q000648:ultrastructure; D008854:Microscopy, Electron / Q000379:methods*; D015203:Reproducibility of Results; D012401:Rotavirus / Q000648:ultrastructure*