Red blood cell tracking using optical flow methods.
Red blood cell tracking using optical flow methods.
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DOI：
10.1109/jbhi.2013.2281915
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发表时间：
2014-05
影响因子：
7.7
通讯作者：
Zhou X
中科院分区：
文献类型：
--
作者：
Guo D;van de Ven AL;Zhou X
微循环的研究是生物医学和生理学研究中的一项重要任务。为了监测人体状况并制定某些疾病的有效治疗方法，必须以无创的方式评估微循环信息，例如流速和血管密度。血细胞自动跟踪作为微循环研究的重要内容之一，是估算血流速度的有效方法。目前，最常用的血细胞跟踪方法是基于时空图像分析的方法，但该方法有很多局限性，如微血管的直径不能大于血细胞或示踪剂的直径，细胞或示踪剂的速度必须固定，对图像质量要求较高等。在本文中，我们提出了一种自动细胞跟踪的光流法。该方法的关键算法是在由各种场景组成的大型图像集中将图像与其相邻图像对齐。考虑到该方法不能解决所有细胞运动情况下的光流问题，本文还提出了另一种光流方法SIFT（Scale Invariant Feature Transform）流。实验结果表明，两种方法都能准确地跟踪细胞。光流对细胞运动速度不稳定的情况具有很强的鲁棒性，而SIFT流则适用于细胞在相邻两帧之间有较大位移的情况。我们提出的方法优于其他方法时，在体内细胞跟踪，它可以用来直接估计血流量，并帮助评估微循环中的其他参数。
The investigation of microcirculation is a critical task in biomedical and physiological research. In order to monitor human’s condition and develop effective therapies of some diseases, the microcirculation information, such as flow velocity and vessel density, must be evaluated in a noninvasive manner. As one of the tasks of microcirculation investigation, automatic blood cell tracking presents an effective approach to estimate blood flow velocity. Currently, the most common method for blood cell tracking is based on spatiotemporal image analysis, which has lots of limitations, such as the diameter of microvesssels cannot be too larger than blood cells or tracers, cells or tracers should have fixed velocity, and it requires the image with high qualification. In this paper, we propose an optical flow method for automatic cell tracking. The key algorithm of the method is to align an image to its neighbors in a large image collection consisting of a variety of scenes. Considering the method cannot solve the problems in all cases of cell movement, another optical flow method, SIFT (Scale Invariant Feature Transform) flow, is also presented. The experimental results show that both methods can track the cells accurately. Optical flow is specially robust to the case where the velocity of cell is unstable, while SIFT flow works well when there are large displacement of cell between two adjacent frames. Our proposed methods outperform other methods when doing in vivo cell tracking, which can be used to estimate the blood flow directly and help to evaluate other parameters in microcirculation.