Multiple Particle Tracking for Live Cell Imaging with Green Fluorescent Protein (GFP) Tagged Videos
Particle tracking is important for understanding the mobile behaviour of objects of varying sizes in a range of physical and biological science applications. In this paper we present a new algorithm for tracking cellular particles imaged using a confocal microscope. The algorithm performs adaptive image segmentation to identify objects for tracking and uses intelligent estimates of neighbourhood search, spatial relationship, velocity, direction estimates, and shape/size estimates to perform robust tracking. Our tracker is tested on three videos for vesicle tracking in GFP tagged videos. The results are compared to the popular Harvard tracker and we show that our tracking scheme offers better performance and flexibility for tracking.
KeywordsParticle tracker Vesicles Insulin Diabetes Confocal Microscopy Image Analysis
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