The blood vessel diameter is often measured in microcirculation studies to quantify the effects of various stimuli. Intravital video microscopy is used to measure the change in vessel diameter by first recording the video and analyzing it using electronic calipers or by using image shearing technique. Manual measurement using electronic calipers or image shearing is time-consuming and prone to measurement error, and automated measurement can serve as an alternative that is faster and more reliable. In this paper, a new feature-based tracking algorithm is presented for automatically measuring diameter of vessels in intravital video microscopy image sequences. Our method tracks the vessel diameter throughout the entire image sequence once the diameter is marked in the first image. The parameters were calibrated using the intravital videos with manual ground truth measurements. The experiment with 10 synthetic videos and 20 intravital microscopy videos, including 10 fluorescence confocal and 10 non-confocal transmission, shows that the measurement can be performed accurately.