Recent development of in vivo microscopy techniques, including green fluorescent proteins, has allowed the visualization of a wide range of dynamic processes in living cells. For quantitative and visual interpretation of such processes, new concepts for time-resolved image analysis and continuous timespace visualization are required. Here, we describe a versatile and fully automated approach consisting of four techniques, namely highly sensitive object detection, fuzzy logic-based dynamic object tracking, computer graphical visualization, and measurement in time-space. Systematic model simulations were performed to evaluate the reliability of the automated object detection and tracking method. To demonstrate potential applications, the method was applied to the analysis of secretory membrane traffic and the functional dynamics of nuclear compartments enriched in pre-mRNA splicing factors.The development of in vivo microscopy techniques and fluorescent reagents has stimulated interest in studying the dynamics of cellular processes (for review, see refs. 1 and 2). These types of experiments generate large and complex data sets and require tools for visual and quantitative analysis of the observed dynamic processes in space and time.Imaging fast moving vesicles in living cells at high speed and high spatial resolution generally implies a low signal-to-noise ratio, hampering accurate object detection. As a consequence of the optical aperture problem, tracking of small objects based on visual similarity criteria is difficult because many objects appear very similar (3). Highly sensitive object detection and tracking has been recognized as crucial for an accurate evaluation of such data. However, a quantitative interpretation of trafficking vesicles has been generally based on manual evaluation of a user-biased selection of objects with apparently highest motility. Such an evaluation is very time-consuming and also is limited by the perception of the manual inspector.Processes in the cell nucleus are much slower and need to be observed over a longer period of time. To avoid disruptions of nuclear processes, the total light exposure during in vivo observation must be minimized. Thus, the signal-to-noise ratio and, more importantly, the number of time series taken in a particular experiment is considerably reduced, leading to a loss in spatiotemporal resolution. Displaying time series as movies is a widely used method for visual interpretation. However, this approach does not improve temporal resolution; that is, additional information about the continuous development of the observed processes between the imaged time steps (subpixel resolution in time) is not obtained. Furthermore, quantitative information is not revealed by such a visual approach. In a first approach to quantitatively describe nuclear dynamics in vivo, single particle tracking (4) has been used to estimate the diffusion of chromatin in living cells of different species (5).Here, we describe a novel technique for fully automated analysis and tim...