Department of Electrical Engineering, Chung Yuan University, Taiwan.
Microvasc Res. 2009 Dec;78(3):319-24. doi: 10.1016/j.mvr.2009.07.002. Epub 2009 Jul 30.
A new approach for the measurement of the red blood cell (RBC) velocity from capillary video by using optical flow estimation has been developed. An image registration function based on mutual information was used for stabilizing images in order to cope with slight finger movement during video acquisition. After image alignment, a skeleton extraction algorithm implemented by thinning was followed which enabled tracking blood flow entirely in arteriolar and venular limbs, and the curved segment as well. Optical flow and cross-correlation approaches were applied individually for velocity estimation of twelve microcirculation videos acquired independently from three healthy volunteers. The RBC velocity of 12 vessels at three given measurement sites (arteriolar, curve and venular sites) in a 45-second period of occlusion-release condition of vessel were examined. There were four stages of flow conditions: resting (T(1)), pre-occlusion (T(2)), post-occlusion (T(3)) and release (T(4)). The results from both approaches revealed that the velocity difference among the three sites were not significant. The pattern of distribution of RBC velocity was also reported. The correlation coefficient (r) of the velocity calculated using optical flow and cross-correlation in four stages of blood flow conditions and the overall correlation were: 1-window: r(T1)=0.68, r(T2)=0.67, r(T3)=0.92, r(T4)=0.88 and r(All)=0.79; 2-window: r(T1)=0.84, r(T2)=0.88, r(T3)=0.87, r(T4)=0.93 and r(All)=0.88. The averaged velocity results showed no significant differences between optical flow and 2-window cross-correlation in all flow conditions. Optical flow estimation is not only independent to the direction of flow, but also able to calculate the intensity displacement of all pixels. The proposed velocity measurement system has been shown to provide complete velocity information for the whole vessel limb which demonstrates the advantage of measuring blood flow at the level of microcirculation more accurately.
一种新的方法，用于通过使用光流估计来测量毛细血管视频中的红细胞（RBC）速度，已经被开发出来。基于互信息的图像配准函数被用于稳定图像，以应对视频采集过程中手指的轻微移动。在图像对齐之后，采用细化的骨架提取算法来跟踪整个动静脉分支和弯曲段的血流。光流和互相关方法分别应用于从三个健康志愿者独立获取的 12 个微循环视频的速度估计。在血管闭塞释放条件下的 45 秒时间段内，检查了 12 个血管在三个指定测量部位（动脉、弯曲和静脉部位）的 RBC 速度。有四个阶段的血流条件：静止（T(1)）、预闭塞（T(2)）、后闭塞（T(3)）和释放（T(4)）。两种方法的结果都表明，三个部位之间的速度差异不显著。还报告了 RBC 速度的分布模式。在四个血流条件阶段和整体相关性中，使用光流和互相关计算的速度的相关系数（r）为：1 窗口：r(T1)=0.68，r(T2)=0.67，r(T3)=0.92，r(T4)=0.88 和 r(All)=0.79；2 窗口：r(T1)=0.84，r(T2)=0.88，r(T3)=0.87，r(T4)=0.93 和 r(All)=0.88。在所有血流条件下，光流估计的平均速度结果与 2 窗口互相关没有显著差异。光流估计不仅独立于流向，而且能够计算所有像素的强度位移。所提出的速度测量系统已被证明能够提供整个血管分支的完整速度信息，这表明在微循环水平上更准确地测量血流的优势。
