A parameter-optimizing model-based approach to the analysis of low-SNR image sequences for biological virus detection

This thesis presents the multi-objective parameter optimization of a novel image analysis process. The focus of application is automatic detection of nano-objects, for example biological viruses, in real-time. Nano-objects are detected by analyzing time series of images recorded with the PAMONO biosensor, after parameters have been optimized on synthetic data created by a signal model for PAMONO. PAMONO, which is short for Plasmon-Assisted Microscopy of Nano-Sized Objects, is a biosensor yielding indirect proofs for objects on the nanometer-scale by measuring the Surface Plasmon Resonance (SPR) effects they cause on the micrometer scale. It is an optical microscopy technique enabling the detection of biological viruses and other nano-objects within a portable device. The PAMONO biosensor produces time series of 2-D images on the order of 4000 half-megapixel images per experiment. A particular challenge for automatic analysis of this data emerges from its low Signal-to-Noise Ratio (SNR). Manual analysis takes approximately two days per experiment and analyzing person. With the automatic analysis process developed in this thesis, occurrences of nano-objects in PAMONO data can be counted and displayed in real-time while measurements are being taken. Analysis is divided into a GPU-based detector aiming at high sensitivity, complemented with a machine learning-based classifier aiming at high precision. The analysis process is embedded into a multi-objective optimization approach that automatically adapts algorithm choice and parameters to changes in physical sensor parameters. Such changes occur, for example, during sensor prototype development. In order to automatically evaluate the objectives undergoing optimization, a signal model for the PAMONO sensor is proposed, which serves to synthesize ground truth-annotated data. The parameters of the analysis process are optimized on this synthetic data, and the classifier is learned from it. Hence, the signal model must accurately mimic the data recorded by the sensor, which is achieved by incorporating real sensor data into synthesis. Both, optimized parameters and the learned classifier, achieve high quality results on the real sensor data to be analyzed: Nano-objects with diameters down to 100 nm are detected reliably in PAMONO data. Note that the median SNR over all nano-objects to be detected was below two in the examined experiments with 100 nm objects. While the presented analysis process can be used for real-time virus detection in PAMONO data, the optimization approach can serve in accelerating the advancement of the sensor prototype towards a final setup of its physical parameters: In this scenario, frequent changes in physical sensor parameters make the automatic adaptation of algorithmic process parameters a desirable goal. No expertise concerning the underlying algorithms is required in these use cases, enabling ready applicability in a lab scenario.