Abstract
In this paper, a procedure is described for tracking moving object trajectories from image sequences acquired from a microfluidic culture platform. Since particles move along the axons, curve structures need to be detected first from the input image sequence. A kymograph analysis technique is applied to detect axon structures from the consolidated image of the input sequence. Horizontally and vertically oriented axons are then detected by applying the process twice to the original and the 90-degree rotated image. Multiple kymographs are generated along the detected axons by projecting image intensity variation through the time-axis. The trajectory detection process is then applied to each kymograph image. To obtain the particle motion information from the entire image sequence, an integration process is applied to each horizontal and vertical kymograph data set. The proposed technique has been applied to image sequences in the present application area. It is demonstrated that practical results can be obtained using time-lapse image sequence data.
Highlights
Time-lapse video of cellular motion in microfluidic culture platform consists of microscopic images acquired at fixed time intervals [1],[2],[3]
While the input is a 2D image defined on (x, y) plane, by regarding the vertical direction as the time axis, vertically oriented axons can be detected by a kymograph analysis process
Once axons are detected from input image sequences, multiple kymographs are generated along the detected axons
Summary
Time-lapse video of cellular motion in microfluidic culture platform consists of microscopic images acquired at fixed time intervals [1],[2],[3]. It is important to trace cellular motions appearing as a collection of irregular movements of small particles. Tracking mitochondria in neural time-lapse video is a basic processing step in diverse biological research [4],[5]. For tracing various intracellular objects in cell imaging, multi-target tracking methods [6],[7] have been exploited previously. Over half of the particles are stationary, and some target objects move at low speeds. Frequent merging and splitting of stationary and moving particles, and sudden starting and stopping of moving targets make it hard to trace individual objects. It is hard to identify individual particle based on shape and brightness information only
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