Automatic Cell Tracking of Microscopic Imaging of Living Cells by a novel Similarity-based Tracking Algorithm, and the Description of the Kinetics of the Cells for Image-Mining

Live-cell assays are used to study the dynamic functional cellular processes in High-Content Screening (HCA) of drug discovery processes or in computational biology experiments. The large amount of image data created during the screening requires automatic image-analysis procedures that can describe these dynamic processes. One class of tasks in this application is the tracking of cells. We describe in this paper a fast and robust cell tracking algorithm applied to HighContent Screening in drug discovery or computational biology experiments. We developed a similarity-based tracking algorithm that can track the cells without an initialization phase of the parameters of the tracker. The similarity-based detection algorithm is robust enough to find similar cells although small changes in the cell morphology have been occurred. The cell tracking algorithm can track normal cells as well as mitotic cells by classifying the cells based on our previously developed texture classifier. Results for the cell path are given on a test series from a real drug discovery process. We present the path of the cell and the low-level features that describe the path of the cell. This information can be used for further image mining of high-level descriptions of the kinetics of the cells.