Lee Jaesung, Jirapatnakul Artit C, Reeves Anthony P, Crowe William E, Sarelius Ingrid H
School of Electrical and Computer Engineering, Cornell University, 357 Rhodes Hall, Ithaca, NY 14850, USA.
Ann Biomed Eng. 2009 May;37(5):913-26. doi: 10.1007/s10439-009-9666-5. Epub 2009 Mar 11.
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.
在微循环研究中，血管直径常被测量以量化各种刺激的效果。活体视频显微镜用于测量血管直径的变化，方法是先记录视频，然后使用电子卡尺进行分析，或者使用图像剪切技术。使用电子卡尺或图像剪切进行手动测量既耗时又容易出现测量误差，而自动测量可以作为一种更快、更可靠的替代方法。本文提出了一种基于新特征的跟踪算法，用于自动测量活体视频显微镜图像序列中的血管直径。一旦在第一幅图像中标记出直径，我们的方法就能在整个图像序列中跟踪血管直径。使用带有手动地面真值测量的活体视频对参数进行了校准。对10个合成视频和20个活体显微镜视频（包括10个荧光共聚焦和10个非共聚焦透射视频）进行的实验表明，该测量可以准确进行。
