||CBA together with the industrial partners Vironova AB (Stockholm) and Delong Instruments
(Czech Republic) have a joint research project with the goal of developing a table-top TEM with
incorporated software for automatic detection and identication of viruses. A method for segmenting potential virus particles in the images has been developed as has various measures of
characteristic features, mainly based on texture, for distinguishing between different virus types.
Different virus species generally have dffierent sizes and shapes but their width (diameter if approximately spherical) is a rather conserved feature as is the protein structure on their surface
(seen as texture patterns in the images).
In the project they currently focus on using different texture measures calculated on a disk centered within an object for classifying the virus species. Extracted feature measures calculated
for one position for (at least) 100 objects of 15 different classes of viruses exist for use in this
project. The aim of this thesis is to investigate if/how feature vectors calculated in multiple
positions can be used to improve the classification. Since the viruses have very different shapes,
from approximately spherical to highly pleomorphic (like boiled spaghetti), the number of possible positions for extracting feature vector will be different for different virus objects. Another
goal is to investigate how the distribution of measures calculated on small patches within the
disk shaped feature area can be used in the classification instead of as we do know combining
them into one measure on the feature vector