IEEE Transactions on Pattern Analysis and Machine Intelligence
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As the primary GLUT isoform in muscle and fat cells, glucose transporter 4 (GLUT4) is responsible for insulin-stimulated glucose uptake into these tissues. Among this procedure, the docking/fusion process was regarded as the most important process which GLUT4 storage vesicles packed with GLUT4s were moving from intracellular compartments to the plasma membrane. One efficient and powerful approach to study the kinetics of GLUT4 translocation is to visualize the dynamic behavior of GLUT4 vesicles by the total internal reflection fluorescence (TIRF) microscopy (TIRFM) system. However, analysis of TIRFM images is currently done in a manual manner which is a very computationally intensive and time-consuming procedure. This paper presents a simple and computationally efficient method which permits the automatic fusion identification. It consists of several algorithms including image segmentation, trajectory linking and vesicle recognition. The efficiency of the method was validated by several image stacks taken by TIRFM. This method is very suitable for the implementation of the large scale biological data analysis. Importantly, our work provides a new idea of studying the insulin regulated GLUT4 translocation and give the prospect of further analysis in quantitative measurement, data mining, etc.