CELL TRACKING AND DATA ANALYSIS OF IN VITRO TUMOUR CELLS FROM TIME-LAPSE IMAGE SEQUENCES

摘要

In this paper, we address the problem of the analysis of cellular phenotype from time-lapse image sequences using object tracking algorithms and feature extraction and classification. We discusses the application of an object tracking algorithm for in the analysis of high content cell-migration time-lapse image sequence of extremely motile cells; these cells are captured at low time-resolution. The small size of the objects and significant deformation of the object during the process renders the tracking as a non-trivial problem. To that end, the 'KDE Mean Shift', a real-time tracking solution, is adapted for our research. We illustrate that in a simulation experiment with artificial objects, with our algorithm an accuracy of over 90% can be established. Based on the tracking result, we propose several morphology and motility based measurements for the analysis of cell behaviour. Our analysis requires only initial manual interference; the majority of the processing is automated.