UmUTracker: A versatile MATLAB program for automated particle tracking of 2D light microscopy or 3D digital holography data
UmUTracker: A versatile MATLAB program for automated particle tracking of 2D light microscopy or 3D digital holography data
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DOI：
10.1016/j.cpc.2017.05.029
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发表时间：
2017-10-01
影响因子：
6.3
通讯作者：
Andersson, Magnus
中科院分区：
文献类型：
--
作者：
Zhang, Hanging;Stangner, Tim;Andersson, Magnus
我们提出了一个通用的和快速的MATLAB程序（UmUTracker），自动检测和跟踪颗粒通过分析视频序列获得的光学显微镜或数字在线全息显微镜。我们的程序检测粒子的二维横向位置与等腰三角形变换的基础上的算法，并重建其三维轴向位置的瑞利-索末菲模型使用径向强度分布的快速实现。为了验证我们的程序的准确性和性能，我们首先使用明场和数字全息显微镜跟踪聚苯乙烯颗粒的2D位置。其次，我们通过分析合成和实验获得的全息图来确定3D粒子位置。最后，为了突出完整的程序功能，我们在100 gm高流量室中对微流体流动进行了分析。这一结果与计算流体动力学模拟一致。在普通台式计算机上，UmUTracker可以在1024 x 1024图像中以每秒5帧的速度检测、分析和跟踪多个粒子，模板大小为201 x 201。为了增强可用性，并使其易于实现新的功能，我们使用面向对象的编程。UmUTracker是适用于相关的研究：粒子动力学，细胞定位，胶体和微流体流动测量程序概要程序标题：UmUTracker程序文件doi：http://dx.doi.org/10.17632/fkprs4s6xp.1Licensing规定：知识共享由4.0（CC由4.0）编程语言：MATLAB问题的性质：3D多粒子跟踪是一种常见的技术在物理，化学和生物学。然而，在准确性方面，可靠的颗粒跟踪是一项具有挑战性的任务，因为结果取决于样品照明，颗粒重叠，运动模糊和来自记录传感器的噪声。另外，如果例如执行计算上昂贵的过程，诸如从数字全息显微镜数据重构轴向粒子位置，则计算性能也是一个问题。解决方案：UmUTracker是一个多功能的工具，可以从光学显微镜或数字全息显微镜采集的长视频序列中提取颗粒位置。该程序提供了一个易于使用的图形用户界面（GUI），用于跟踪和后处理，不需要任何编程技能来分析粒子跟踪实验的数据。UmUTracker首先进行自动2D粒子检测，即使在嘈杂的条件下，使用一种新的圆形检测器的基础上等腰三角形采样技术与多尺度策略。为了减少3D跟踪的计算量，它使用了瑞利-索末菲光传播模型的有效实现。为了分析和可视化数据，包括有效的数据分析步骤，其可以例如使用3D轨迹示出4D流动可视化。此外，由于面向对象的编程风格，UmUTracker很容易通过用户自定义模块进行修改其他评论：程序可从https://sourceforge.net/projects/umutracker/获得（C）2017 Elsevier B. V.保留所有权利。
We present a versatile and fast MATLAB program (UmUTracker) that automatically detects and tracks particles by analyzing video sequences acquired by either light microscopy or digital in-line holographic microscopy. Our program detects the 2D lateral positions of particles with an algorithm based on the isosceles triangle transform, and reconstructs their 3D axial positions by a fast implementation of the Rayleigh-Sommerfeld model using a radial intensity profile. To validate the accuracy and performance of our program, we first track the 2D position of polystyrene particles using bright field and digital holographic microscopy. Second, we determine the 3D particle position by analyzing synthetic and experimentally acquired holograms. Finally, to highlight the full program features, we profile the microfluidic flow in a 100 gm high flow chamber. This result agrees with computational fluid dynamic simulations. On a regular desktop computer UmUTracker can detect, analyze, and track multiple particles at 5 frames per second for a template size of 201 x 201 in a 1024 x 1024 image. To enhance usability and to make it easy to implement new functions we used object-oriented programming. UmUTracker is suitable for studies related to: particle dynamics, cell localization, colloids and microfluidic flow measurement.Program summaryProgram title: UmUTrackerProgram Files doi: http://dx.doi.org/10.17632/fkprs4s6xp.1Licensing provisions: Creative Commons by 4.0 (CC by 4.0)Programming language: MATLAB Nature of problem: 3D multi-particle tracking is a common technique in physics, chemistry and biology. However, in terms of accuracy, reliable particle tracking is a challenging task since results depend on sample illumination, particle overlap, motion blur and noise from recording sensors. Additionally, the computational performance is also an issue if, for example, a computationally expensive process is executed, such as axial particle position reconstruction from digital holographic microscopy data. Versatile robust tracking programs handling these concerns and providing a powerful post-processing option are significantly limited.Solution method: UmUTracker is a multi-functional tool to extract particle positions from long video sequences acquired with either light microscopy or digital holographic microscopy. The program provides an easy-to-use graphical user interface (GUI) for both tracking and post-processing that does not require any programming skills to analyze data from particle tracking experiments. UmUTracker first conduct automatic 2D particle detection even under noisy conditions using a novel circle detector based on the isosceles triangle sampling technique with a multi-scale strategy. To reduce the computational load for 3D tracking, it uses an efficient implementation of the Rayleigh-Sommerfeld light propagation model. To analyze and visualize the data, an efficient data analysis step, which can for example show 4D flow visualization using 3D trajectories, is included. Additionally, UmUTracker is easy to modify with user customized modules due to the object-oriented programming styleAdditional comments: Program obtainable from https://sourceforge.net/projects/umutracker/ (C) 2017 Elsevier B.V. All rights reserved.