Estimation of real-time red blood cell velocity in conjunctival vessels using a modified dynamic-time-warping approach

We developed a tool to automatically analyse video sequences of conjunctival vessels, digitally imaged with high enough magnification to resolve movement of the blood within the vessel. After registering each frame of the sequence in order to compensate the movement of the patients or of the imaging instrumentation, we automatically tracked all vessels. From each vessel and from each frame we extract a one-dimensional signal representing the longitudinal variation of gray level along the vessel that is related to the presence of red blood cells. Then we estimate the local shift of the signals of a vessel between different frames, using a modified dynamic-time-warping approach. We test the algorithm first on simulated vessels, where the mean cell velocity is known, and on real video sequences. We show the effectivity of our method both regarding the estimation error and comparing it with a simpler cross-correlation approach, showing the possibility to design and develop a system to non-invasively quantify the blood velocity in the conjunctival vessels.