  Detection of relative changes in circulating blood volume is important to
guide resuscitation and manage variety of medical conditions including sepsis,
trauma, dialysis and congestive heart failure. Recent studies have shown that
estimates of circulating blood volume can be obtained from ultrasound imagery
of the of the internal jugular vein (IJV). However, segmentation and tracking
of the IJV is significantly influenced by speckle noise and shadowing which
introduce uncertainty in the boundaries of the vessel. In this paper, we
investigate the use of optical flow algorithms for segmentation and tracking of
the IJV and show that the classical Lucas-Kanade (LK) algorithm provides the
best performance among well-known flow tracking algorithms.
