Microtubule Dynamics Analysis Using Kymographs and Variable-Rate Particle Filters
Microtubule Dynamics Analysis Using Kymographs and Variable-Rate Particle Filters
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DOI：
10.1109/tip.2010.2045031
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发表时间：
2010-07-01
影响因子：
10.6
通讯作者：
Meijering, Erik
中科院分区：
文献类型：
--
作者：
Smal, Ihor;Grigoriev, Ilya;Meijering, Erik
研究细胞内动力学对于在分子水平上理解健康生命和开发针对疾病过程的药物具有根本重要性。使这项研究的关键技术之一是自动跟踪和运动分析这些对象的显微图像序列。为了更好地利用时空信息比常见的逐帧跟踪方法，最近提出了两种替代方法，基于贝叶斯估计或时空分割。在本文中，我们提出了联合收割机的权力，这两种方法，并开发了一种新的概率方法来分割的痕迹的运动物体的记波表示的图像数据。它是基于可变速率粒子滤波和使用多尺度趋势分析提取的痕迹，以估计相关的运动学参数。逼真的合成生成的图像，以及对真实的生物图像数据的实验证明了改进的潜力的新方法在体外微管动力学的分析。
Studying intracellular dynamics is of fundamental importance for understanding healthy life at the molecular level and for developing drugs to target disease processes. One of the key technologies to enable this research is the automated tracking and motion analysis of these objects in microscopy image sequences. To make better use of the spatiotemporal information than common frame-by-frame tracking methods, two alternative approaches have recently been proposed, based upon either Bayesian estimation or space-time segmentation. In this paper, we propose to combine the power of both approaches, and develop a new probabilistic method to segment the traces of the moving objects in kymograph representations of the image data. It is based on variable-rate particle filtering and uses multiscale trend analysis of the extracted traces to estimate the relevant kinematic parameters. Experiments on realistic synthetically generated images as well as on real biological image data demonstrate the improved potential of the new method for the analysis of microtubule dynamics in vitro.