Adaptive settings for the nearest-neighbor particle tracking algorithm
Adaptive settings for the nearest-neighbor particle tracking algorithm
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DOI：
10.1093/bioinformatics/btu793
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发表时间：
2015-04-15
期刊：
影响因子：
5.8
通讯作者：
Costantino, Santiago
中科院分区：
文献类型：
--
作者：
Mazzaferri, Javier;Roy, Joannie;Costantino, Santiago
背景：单粒子跟踪（SPT）最近邻算法的性能取决于需要根据所研究的时间序列的特征设置的参数。当全局设置参数时，这些特性在空间上波动的非均匀系统的跟踪效果很差。结果：我们提出了一种新颖的 SPT 方法，该方法将著名的最近邻跟踪算法适应粒子的局部密度，以克服不均匀性问题。结论：我们使用数值模拟和实验数据证明了所提出的方法所提供的性能改进，并将其性能与 最先进的 SPT 算法。
Background: The performance of the single particle tracking (SPT) nearest-neighbor algorithm is determined by parameters that need to be set according to the characteristics of the time series under study. Inhomogeneous systems, where these characteristics fluctuate spatially, are poorly tracked when parameters are set globally.Results: We present a novel SPT approach that adapts the well-known nearest-neighbor tracking algorithm to the local density of particles to overcome the problems of inhomogeneity.Conclusions: We demonstrate the performance improvement provided by the proposed method using numerical simulations and experimental data and compare its performance with state of the art SPT algorithms.