We present a quantitative comparison of three categories of velocity estimation algorithms, including centroid techniques (the adaptive centroid technique and the weighted centroid technique), the sliding-window filtering technique, and correlation techniques (autocorrelation and cross correlation). We introduce, among these five algorithms, two new algorithms: weighted centroid and sliding-window filtering. Simulations and <i>in vivo</i> blood flow data are used to assess the velocity estimation accuracies of these algorithms. These comparisons demonstrate that the sliding-window filtering technique is superior to the other techniques in terms of velocity estimation accuracy and robustness to noise.
© 2002 Optical Society of America
(100.2000) Image processing : Digital image processing
(170.1650) Medical optics and biotechnology : Coherence imaging
(170.3880) Medical optics and biotechnology : Medical and biological imaging
(170.4500) Medical optics and biotechnology : Optical coherence tomography
Daqing Piao, Linda L. Otis, Niloy K. Dutta, and Quing Zhu, "Quantitative Assessment of Flow Velocity-Estimation Algorithms for Optical Doppler Tomography Imaging," Appl. Opt. 41, 6118-6127 (2002)