Morphological filter improve the efficiency of automated tracking of secretory vesicles with various dynamic properties.

Membrane trafficking is a very important physiological process involved in protein transport, endocytosis, and exocytosis. The functions of vesicles are strongly correlated with various spatial dynamic properties of vesicles, including their types of movements and morphology. Several methods are used to quantify such dynamic properties, but most of them are specific to particular populations of vesicles. We previously developed the so-called PTrack system for quantifying the dynamics of secretory vesicles near the cell surface, which are small and move slowly. To improve the system performance in quantifying large and fast-moving vesicles, we firstly combined morphological filter with two-threshold image processing techniques to locate granules of various sizes. Next, Kalman filtering was used to improve the performance in tracking fast-moving and large granules. Performance evaluation by using simulation image sequences shown that the new system, called PTrack II, yields better tracking accuracy. The tracking system was validated using time-lapse images of insulin granules in betaTC3 cells, which revealed that PTrack II could track better than PTrack, averaged accuracy up to 56%. The overall tracking results indicate that PTrack II is better at tracking vesicles with various dynamic properties, which will facilitate the acquisition of more-complete information on vesicle dynamics.