Investigating Multi Instance Classifiers for improved virus classification in TEM images

Abstract: 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 identification 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 different 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, rather than combining them into one measure as is currently done.