Improving Nanoobject Detection in Optical Biosensor Data

The importance of real-time capable mobile biosensors increases in face of rising numbers of global virus epidemics. Such biosensors can be used for on-site diagnosis, e.g. at airports, to prevent further spread of virus-transmitted diseases, by answering the question whether or not a sample contains a certain virus. In-depth laboratory analysis might furthermore demand for measurements of the concentration of virus particles in a sample. The novel PAMONO sensor technique allows for accomplishing both tasks. One of its basic prerequisites is an efficient analysis of the biosensor image data by means of digital image processing and classification. In this study, we present a high performance approach to this analysis: The diagnosis whether a virus occurs in the sample can be carried out in real-time with high accuracy. An estimate of the concentration can be obtained in real-time as well, if that concentration is not too high. The contribution of this work is an optimization of our processing pipeline used for PAMONO sensor data analysis. The following objectives are optimized: detection-quality, speed and consumption of resources (e.g. energy, memory). Thus our approach respects the constraints imposed by medical applicability, as well as the constraints on resource consumption arising in embedded systems. The parameters to be optimized are descriptive (virus appearance parameters) and hardware-related (design space exploration).