Xie Jun, Khan Shahid, Shah Mubarak
Janelia Farm Research Campus, HHMI, USA.
Med Image Comput Comput Assist Interv. 2008;11(Pt 1):824-32. doi: 10.1007/978-3-540-85988-8_98.
In this paper, we present an automatic method for estimating the trajectories of Escherichia coli bacteria from in vivo phase-contrast microscopy videos. To address the low-contrast boundaries in cellular images, an adaptive kernel-based technique is applied to detect cells in sequence of frames. Then a novel matching gain measure is introduced to cope with the challenges such as dramatic changes of cells' appearance and serious overlapping and occlusion. For multiple cell tracking, an optimal matching strategy is proposed to improve the handling of cell collision and broken trajectories. The results of successful tracking of Escherichia coli from various phase-contrast sequences are reported and compared with manually-determined trajectories, as well as those obtained from existing tracking methods. The stability of the algorithm with different parameter values is also analyzed and discussed.
在本文中，我们提出了一种从体内相差显微镜视频中估计大肠杆菌轨迹的自动方法。为了解决细胞图像中低对比度边界的问题，应用了一种基于自适应核的技术来检测帧序列中的细胞。然后引入了一种新颖的匹配增益度量来应对诸如细胞外观的剧烈变化以及严重的重叠和遮挡等挑战。对于多细胞跟踪，提出了一种最优匹配策略以改进对细胞碰撞和轨迹中断的处理。报告了从各种相差序列成功跟踪大肠杆菌的结果，并与手动确定的轨迹以及从现有跟踪方法获得的轨迹进行了比较。还分析和讨论了算法在不同参数值下的稳定性。
