Zhang Guanglie, Ouyang Mengxing, Mai John, Li Wen Jung, Liu Wing Keung
City University of Hong Kong, Dept. MBE, Kowloon Tong, Hong Kong.
J Lab Autom. 2013 Apr;18(2):161-70. doi: 10.1177/2211068212468582. Epub 2012 Nov 27.
This article describes an automated rotation rate tracking algorithm for pigmented cells that undergo rotation in a dielectrophoretic (DEP) force field. In a completely automated process, we preprocess each frame of a video sequence, then analyze the sequence frame by frame using a rotating-circle template with a block-matching algorithm, and finally estimate the rotation rate of the pigmented cells using a pixel-patch correlation. The algorithm has been demonstrated to accurately calculate the DEP-induced rotation rate of the cell up to 250 rpm. Cell rotation rates in various DEP force fields (i.e., by varying the applied voltages, frequencies, and waveforms to induce different force fields) were analyzed using this automated algorithm and reported in this article. Most importantly, the algorithm is accurate even when the cells have simultaneous translational and rotational motions across the video image sequence. Also, the algorithm is capable of tracking changes in rotation speed over a long period of time (90 s) by stably analyzing a massive data set of video image frames.
本文描述了一种用于在介电泳（DEP）力场中发生旋转的色素细胞的自动旋转速率跟踪算法。在一个完全自动化的过程中，我们对视频序列的每一帧进行预处理，然后使用具有块匹配算法的旋转圆模板逐帧分析该序列，最后使用像素块相关性估计色素细胞的旋转速率。该算法已被证明能够准确计算细胞在高达250转/分钟的DEP诱导旋转速率。使用这种自动算法分析了各种DEP力场中的细胞旋转速率（即通过改变施加的电压、频率和波形来诱导不同的力场），并在本文中进行了报道。最重要的是，即使细胞在视频图像序列中同时进行平移和旋转运动，该算法也能保持准确。此外，该算法能够通过稳定分析大量视频图像帧数据集来跟踪长时间（90秒）内的旋转速度变化。
