Sliding Fractal Dimensions of Subviral Particles in Fluorescence Image Sequences

Abstract

The research for new medicines against pathogens of hemorrhagic fevers like the Ebola or Marburg virus is a work intensive field in pharmaceutical science. It requires an accurate knowledge of the pathogens' behaviors to assess the influences of new potential medicines on them. In cooperation between the Virology of the Philipps-University in Marburg and the Institute for Biomedical Engineering at the University for Applied Science in Gießen new algorithms for automatic detection and classification of subviral particles in fluorescence image sequences were introduced. In this article a new method to analyze and classify the temporal variations of motion patterns of subviral particles is presented. The fractal dimensions within sliding time windows reveal the dynamic changes of the particles motion-steadiness. This allows making statements about time dependent influences of unknown substances to infected cells. This article describes the new method and delivers a proof of concept. First results on real data show a good potential of this method to assist pharmaceutical research. The article ends with a discussion and conclusion.