High-throughput screening using cell images is an efficient method for
screening new candidates for pharmaceutical drugs. To complete the screening
process, it is essential to have an efficient process for analyzing cell
images. This paper presents a new method for efficiently tracking cells and
quantitatively detecting the signal ratio between cytoplasm and nuclei.
Existing methods include those that use image processing techniques and those
that utilize artificial intelligence (AI). However, these methods do not
consider the correspondence of cells between images, or require a significant
amount of new learning data to train AI. Therefore, our method uses automatic
thresholding and labeling algorithms to compare the position of each cell
between images, and continuously measure and analyze the signal ratio of cells.
This paper describes the algorithm of our method. Using the method, we
experimented to investigate the effect of the number of opening and closing
operations during the binarization process on the tracking of the cells.
Through the experiment, we determined the appropriate number of opening and
closing processes.

高通量筛选利用细胞图像是筛选新药候选物的有效方法，本文介绍了一种高效追踪细胞并定量检测胞浆与细胞核信号比率的新方法，通过自动阈值与标签算法比较不同图像中细胞的位置，进行连续测量与分析细胞信号比率，进一步研究开闭运算次数对细胞追踪的影响，确定了合适的开闭运算过程次数。