Fractal Characterization of Subviral Particle Motion: On the Influence of Spatio-Temporal Interpolation Methods

Due to the rapid globalization there is an increasing danger for pandemic outbreaks. The high death toll of fast spreading diseases like the Ebola infection demand the fast development of new medicines. Thus, the automation of pharmaceutical processes is an indispensable but challenging task. In cooperation with the Institute for Virology, Philipps-University, Marburg, Germany, recently, algorithms to detect and evaluate subviral particle tracks in live-cell fluorescence image sequences were developed. In steady interdisciplinary exchange between pharmacists and engineers it turned out that new measures to identify and classify subviral particle motion are required. This article focuses on the evaluation and optimization of a new method to classify subviral particle motion using fractal dimension estimation. The influence of global and local interpolation methods on fractal dimension estimation is investigated. The methods are tested on simulated data and applied to real image sequences. The results prospect a high benefit of using the presented methods for an effective classification of subviral particle behavior.