Feature point tracking and trajectory analysis for video imaging in cell biology
Feature point tracking and trajectory analysis for video imaging in cell biology
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DOI：
10.1016/j.jsb.2005.06.002
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发表时间：
2005-08-01
影响因子：
3
通讯作者：
Koumoutsakos, P
中科院分区：
文献类型：
--
作者：
Sbalzarini, IF;Koumoutsakos, P
本文提出了一种计算效率高，二维，特征点跟踪算法的自动检测和定量分析的颗粒轨迹记录在细胞生物学中的视频成像。跟踪过程不需要运动的先验数学建模，它是自初始化的，它辨别虚假检测，并且它可以处理临时遮挡以及粒子从图像区域出现和消失。该算法的效率进行了验证合成视频数据，它是比较现有的方法和它的准确性和精度进行评估，为广泛的信噪比。该算法非常适合于依赖于低强度荧光显微镜的细胞生物学视频成像。它的适用性在三个案例研究中得到了证明，涉及内体中低密度脂蛋白的运输，荧光标记的腺病毒-2颗粒沿着微管的运动，以及活细胞质膜上的量子点的跟踪。本发明的自动跟踪过程使得能够使用诸如矩标度光谱的技术来量化细胞生物学中的分散过程。(C)2005年爱思唯尔公司All rights reserved.
This paper presents a computationally efficient, two-dimensional, feature point tracking algorithm for the automated detection and quantitative analysis of particle trajectories as recorded by video imaging in cell biology. The tracking process requires no a priori mathematical modeling of the motion, it is self-initializing, it discriminates spurious detections, and it can handle temporary occlusion as well as particle appearance and disappearance from the image region. The efficiency of the algorithm is validated on synthetic video data where it is compared to existing methods and its accuracy and precision are assessed for a wide range of signal-to-noise ratios. The algorithm is well suited for video imaging in cell biology relying on low-intensity fluorescence microscopy. Its applicability is demonstrated in three case studies involving transport of low-density lipoproteins in endosomes, motion of fluorescently labeled Adenovirus-2 particles along microtubules, and tracking of quantum dots on the plasma membrane of live cells. The present automated tracking process enables the quantification of dispersive processes in cell biology using techniques such as moment scaling spectra. (C) 2005 Elsevier Inc. All rights reserved.