Genetically-encoded biosensors based on fluorescence resonance energy transfer (FRET) have been widely applied to study the spatiotemporal regulation of molecular activity in live cells with high resolution. The efficient and accurate quantification of the large amount of imaging data from these single-cell FRET measurements demands robust and automated data analysis. However, the nonlinear movement of live cells presents tremendous challenge for this task. Based on image registration of the single-cell movement, we have developed automated image analysis methods to track and quantify the FRET signals within user-defined subcellular regions. In addition, the subcellular pixels were classified according to their associated FRET signals and the dynamics of the clusters analyzed. The results revealed that the EGF-induced reduction of RhoA activity in migratory HeLa cells is significantly less than that in stationary cells. Furthermore, the RhoA activity is polarized in the migratory cells, with the gradient of polarity oriented toward the opposite direction of cell migration. In contrast, there is a lack of consistent preference in RhoA polarity among stationary cells. Therefore, our image analysis methods can provide powerful tools for high-throughput and systematic investigation of the spatiotemporal molecular activities in regulating functions of live cells with their shapes and positions continuously changing in time.