Abstract：Developing algorithms for plant cell tracking in microscopic image sequences is very critical to the modeling of cell growth pattern and gene expression dynamics. The plant cells are tightly clustered in space and have very similar shapes and intensity distributions, and the images can be translated, rotated in the imaging process, thus tracking plant cell across image sequences is very challenging. This paper proposed a dynamic local graph matching method, which efficiently exploited the feature of the cells area, the angle and distance between adjacent cells to match the plant cells. The most similar cell pair was chosen as the seed cell pair by computing the feature distances between the cells in two adjacent images, and then their neighboring cells were gradually matched starting from the seed pair. During the dynamic local graph matching process, for each iteration, the matched cells were regarded as newly added seed cells, and the cells in the dynamically updated neighborhood with the least feature distance were matched firstly. Experimental results on three unregistered plant cell (Shoot Apical Meristem, SAM) image sequences and their registered image sequences showed that the proposed method improved the tracking accuracy rate by 4% in the registered image sequences and by 30% in the unregistered image sequences when compared with the existing plant cell tracking method. In conclusion, the method is valuable for plant cell population tracking in microscopic image data.
钱伟丽, 刘敏, 李洁沁, 刘小燕. 基于局部图动态匹配的植物细胞追踪算法研究[J]. 中国生物医学工程学报, 2018, 37(6): 649-656.
Qian Weili, Liu Min, Li Jieqin, Liu Xiaoyan. Algorithm study of Plant Cell Tracking Based on Dynamic Local Graph Matching. Chinese Journal of Biomedical Engineering, 2018, 37(6): 649-656.
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