基于局部图动态匹配的植物细胞追踪算法研究  

Algorithm study of Plant Cell Tracking Based on Dynamic Local Graph Matching

作　　者：钱伟丽 刘敏[1] 李洁沁 刘小燕[1] Qian Weili;Liu Min;Li Jieqin;Liu Xiaoyan(College of Electrical and Information Engineering,Hunan University,Changsha 410082,China)

机构地区：[1]湖南大学电气与信息工程学院,长沙410082

出　　处：《中国生物医学工程学报》2018年第6期649-656,共8页Chinese Journal of Biomedical Engineering

基　　金：国家自然科学基金(61771189;61301254)

摘　　要：植物细胞追踪算法的研究对建立细胞的生长发育模型并探索其基因的结构和功能至关重要。由于植物细胞拥有相似的形状和灰度分布,在空间上具有紧密相连的特殊结构,且在成像过程中存在严重的噪声干扰,细胞图像可能发生错位或者旋转,给植物细胞的追踪带来了巨大挑战。针对以上难点,提出一种基于局部图动态匹配的细胞追踪方法,细胞面积、相邻细胞之间的夹角与距离被用作匹配的基本特征。通过计算相邻两帧细胞图像中细胞上述特征的距离函数,寻找最相似的细胞对作为种子细胞对,然后通过种子细胞逐步匹配其邻域细胞。在细胞逐步匹配过程中,已匹配的细胞将作为新增加的种子细胞。在动态扩张的已匹配细胞邻域范围中,每次优先匹配特征距离最小的细胞对,通过这种动态匹配方法提高细胞匹配的准确率。算法对3组未配准植物顶端分生组织细胞图像序列及它们的配准图像序列进行追踪实验,结果显示与之前的植物细胞追踪算法相比,在配准图像序列中平均追踪准确率可提高4%,在未配准图像序列中平均追踪准确率可提高30%。实验结果表明,所提出的算法可有效提高细胞追踪的准确率,对显微图像数据中细胞群的追踪具有很好的应用价值。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.

关 键 词：植物细胞 细胞追踪 局部图动态匹配 细胞分裂检测 

分 类 号：R318[医药卫生—生物医学工程]