Biomedical Imaging Group Rotterdam, Department of Medical Informatics, Erasmus MC, Rotterdam, The Netherlands.
IEEE Trans Image Process. 2010 Jul;19(7):1861-76. doi: 10.1109/TIP.2010.2045031. Epub 2010 Mar 11.
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.
研究细胞内动力学对于理解分子水平上的健康生命以及开发针对疾病过程的药物至关重要。实现这一研究的关键技术之一是自动跟踪和分析显微镜图像序列中这些物体的运动。为了比常见的逐帧跟踪方法更好地利用时空信息，最近提出了两种基于贝叶斯估计或时空分割的替代方法。在本文中，我们提出了结合这两种方法的优势，并开发了一种新的概率方法来分割图像数据的共聚焦图像表示中的运动物体的轨迹。它基于变速粒子滤波，并使用提取轨迹的多尺度趋势分析来估计相关的运动学参数。在真实的合成图像以及真实的生物图像数据上的实验表明，该新方法在分析体外微管动力学方面具有改进的潜力。
