Object Tracking and Data Analysis with Time-Lapse Video of in Vitro MTLn3 Cell Lines
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چکیده
This paper considers the problem the improvement and application of the KDE Mean Shift tracking algorithm and data analysis in a migration study of MTLn3 cells. The aim is to convert cell migration videos into numeric description of changes in cell behavior. The choice of KDE Mean Shift Tracking is based on its robust and prominent performance in time-lapse studies compared to other algorithms. Morphology and motility measurements are defined by considering both statistics and true biological translation. The detection of significant behavior change relies on both feature selection and hypothesis testing in the feature space. The feedback from the “wet-lab” to our tracking analysis and results indicate that the accuracy of cell migration analysis is increased significantly and labor time is reduced enormously (over 300%). In addition, it is also believed that the current feature measurements reveal several important behavior measurements, which cannot be determined by manual observation. Supported by the results in cellular analysis we intend to extend our experiments into molecular analysis such as focal adhesions in the near future. This project is conducted under joint operation of Leiden Institute of Advanced Computer Science (LIACS) Leiden University with Leiden/Amsterdam Center of Drug Research (LACDR) Leiden University.
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تاریخ انتشار 2008