Smal I, Meijering E, Draegestein K, Galjart N, Grigoriev I, Akhmanova A, van Royen M E, Houtsmuller A B, Niessen W
Department of Medical Informatics, Erasmus MC-University Medical Center Rotterdam, Rotterdam, The Netherlands.
Med Image Anal. 2008 Dec;12(6):764-77. doi: 10.1016/j.media.2008.03.004. Epub 2008 Mar 31.
Time-lapse fluorescence microscopy imaging has rapidly evolved in the past decade and has opened new avenues for studying intracellular processes in vivo. Such studies generate vast amounts of noisy image data that cannot be analyzed efficiently and reliably by means of manual processing. Many popular tracking techniques exist but often fail to yield satisfactory results in the case of high object densities, high noise levels, and complex motion patterns. Probabilistic tracking algorithms, based on Bayesian estimation, have recently been shown to offer several improvements over classical approaches, by better integration of spatial and temporal information, and the possibility to more effectively incorporate prior knowledge about object dynamics and image formation. In this paper, we extend our previous work in this area and propose an improved, fully automated particle filtering algorithm for the tracking of many subresolution objects in fluorescence microscopy image sequences. It involves a new track management procedure and allows the use of multiple dynamics models. The accuracy and reliability of the algorithm are further improved by applying marginalization concepts. Experiments on synthetic as well as real image data from three different biological applications clearly demonstrate the superiority of the algorithm compared to previous particle filtering solutions.
在过去十年中，延时荧光显微镜成像技术迅速发展，为体内细胞内过程的研究开辟了新途径。此类研究产生了大量有噪声的图像数据，无法通过人工处理进行高效可靠的分析。虽然存在许多流行的跟踪技术，但在高物体密度、高噪声水平和复杂运动模式的情况下，往往无法产生令人满意的结果。基于贝叶斯估计的概率跟踪算法最近被证明比传统方法有几个改进之处，它能更好地整合空间和时间信息，并且有可能更有效地纳入关于物体动态和图像形成的先验知识。在本文中，我们扩展了我们在该领域以前的工作，并提出了一种改进的、完全自动化的粒子滤波算法，用于跟踪荧光显微镜图像序列中的许多亚分辨率物体。它涉及一种新的轨迹管理程序，并允许使用多种动力学模型。通过应用边缘化概念，进一步提高了算法的准确性和可靠性。对来自三种不同生物学应用的合成图像数据和真实图像数据进行的实验清楚地证明了该算法相对于以前的粒子滤波解决方案的优越性。
