IEEE Trans Med Imaging. 2015 Feb;34(2):415-32. doi: 10.1109/TMI.2014.2359541. Epub 2014 Sep 19.
Tracking subcellular structures as well as viral structures displayed as 'particles' in fluorescence microscopy images yields quantitative information on the underlying dynamical processes. We have developed an approach for tracking multiple fluorescent particles based on probabilistic data association. The approach combines a localization scheme that uses a bottom-up strategy based on the spot-enhancing filter as well as a top-down strategy based on an ellipsoidal sampling scheme that uses the Gaussian probability distributions computed by a Kalman filter. The localization scheme yields multiple measurements that are incorporated into the Kalman filter via a combined innovation, where the association probabilities are interpreted as weights calculated using an image likelihood. To track objects in close proximity, we compute the support of each image position relative to the neighboring objects of a tracked object and use this support to recalculate the weights. To cope with multiple motion models, we integrated the interacting multiple model algorithm. The approach has been successfully applied to synthetic 2-D and 3-D images as well as to real 2-D and 3-D microscopy images, and the performance has been quantified. In addition, the approach was successfully applied to the 2-D and 3-D image data of the recent Particle Tracking Challenge at the IEEE International Symposium on Biomedical Imaging (ISBI) 2012.
跟踪亚细胞结构以及荧光显微镜图像中显示为“粒子”的病毒结构，可以提供有关基础动力学过程的定量信息。我们开发了一种基于概率数据关联的跟踪多个荧光粒子的方法。该方法结合了一种定位方案，该方案使用基于点增强滤波器的自下而上策略以及基于使用卡尔曼滤波器计算的高斯概率分布的基于椭圆采样方案的自上而下策略。定位方案产生了多个测量值，这些测量值通过组合创新纳入卡尔曼滤波器，其中关联概率被解释为使用图像似然度计算的权重。为了跟踪近距离的物体，我们计算每个图像位置相对于跟踪物体的相邻物体的支撑，并使用此支撑重新计算权重。为了处理多个运动模型，我们集成了交互多模型算法。该方法已成功应用于合成 2-D 和 3-D 图像以及真实 2-D 和 3-D 显微镜图像，并对性能进行了量化。此外，该方法还成功应用于最近在 2012 年 IEEE 国际生物医学成像研讨会（ISBI）上举行的粒子跟踪挑战赛的 2-D 和 3-D 图像数据。
