This research focuses on the development of novel generic and adaptive motion analysis methods, based on particle filtering techniques, aiming at the development of a unified particle filtering based framework capable of adaptive system identification, object state estimation, and parameter tuning within the Bayesian ideology. The developed framework will be primarily applied to and validated on tracking and motion analysis applications in biology, with contribution to the quantitative understanding of the fundamental processes in living cells and advancing the field of systems biology.
This project concerns the design of an integrated optical scheme using smart optics components to compensate for varying aberrations together with integration of real-time control system for improved scanning and active compensation for aberrations. Additionally, it involves the development of adaptive deconvolution methods for image enhancement as well as detection and tracking methods for image analysis and evaluation of the developed smart microscopy imaging system by application to biomedically relevant research questions will be carried out.
The goal of the project is the development of image processing and analysis methodology for integration of molecular, functional and anatomical imaging data. This includes registration, matching, and correlation of motion at the anatomical level, detection and tracking of functional events at the cellular level and combination of data from multiple imaging modalities. As a result, the developed methodology will enable quantified monitoring of disease progression and treatment responses that is not possible with current, visual interpretation.
The goal of the project is to develop automated image analysis techniques for accurate and reproducible tracking and motion analysis of subcellular structures from time-lapse fluorescence microscopy image data. The results of this research are described in my PhD thesis.