A spatio-temporally integrated and nonlinear particle tracking system for live cell imaging

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Despite recent advances in light microscopy revolutionizing live cell imaging [1-2], the full potential of the increasing high sensitivity and resolution of modern microscopes has yet to be realised. A key barrier is the limited power and scope of image analysis techniques currently available to cell biologists. Although commercial programs promise automated particle identification and tracking and have been successfully used to assist image analysis in high signal to noise ratio environments, our extensive experimental tests have shown that they are inadequate to deal with live cell images that are of low signal to noise ratio, poor and variable contrast, and often comprise multiple particles in close proximity and with inconsistent movement. In many cases, particles can only be tracked manually by placing the cursor on objects over time. A major improvement would be to exploit advanced image processing and analysis algorithms to deal with complex time-lapse live cell data, in much the same way that new de-convolution algorithms have significantly improved wide-field imaging in Biology [3]. In this project, we propose to research and develop a nonlinear partial differential equation (PDE) method as a new approach to tracking biological particles in live cells. A key advantage of this method over all commercial software currently available to biologists is to make full use of temporal and spatial relationships in time-lapse data to assist in overcoming severe noise effects and recognition of targets in real biological conditions. Specifically, we will develop a spatio-temporally integrated and nonlinear particle tracking system based on the PDE approach and integrate it to the ImageJ image analysis suite [4] to provide a user-friendly graphical interface to biologists.