Tang Chunming, Dong Shasha, Ning Yanbo, Cui Ying
Information and Communication Engineering College, Harbin Engineering University, Harbin 150001, China.
Sheng Wu Yi Xue Gong Cheng Xue Za Zhi. 2012 Aug;29(4):597-603.
Analysis of neural stem cells' movements is one of the important parts in the fields of cellular and biological research. The main difficulty existing in cells' movement study is whether the cells tracking system can simultaneously track and analyze thousands of neural stem cells (NSCs) automatically. We present a novel cells' tracking algorithm which is based on segmentation and data association in this paper, aiming to improve the tracking accuracy further in high density NSCs' image. Firstly, we adopted different methods of segmentation base on the characteristics of the two cell image sequences in our experiment. Then we formed a data association and constituted a coefficient matrix by all cells between two adjacent frames according to topological constraints. Finally we applied The Hungarian algorithm to implement inter-cells matching optimally. Cells' tracking can be achieved according to this model from the second frame to the last one in a sequence. Experimental results showed that this approaching method has higher accuracy compared with that using the topological constraints tracking alone. The final tracking accuracies of average of sequence I and sequence II have been improved 10.17% and 4%, respectively.
神经干细胞运动分析是细胞与生物学研究领域的重要组成部分之一。细胞运动研究中存在的主要困难在于细胞追踪系统能否自动同时追踪和分析数千个神经干细胞（NSCs）。本文提出了一种基于分割和数据关联的新型细胞追踪算法，旨在进一步提高高密度神经干细胞图像中的追踪精度。首先，我们根据实验中两个细胞图像序列的特征采用了不同的分割方法。然后，我们形成数据关联，并根据拓扑约束由相邻两帧之间的所有细胞构成一个系数矩阵。最后，我们应用匈牙利算法实现细胞间的最优匹配。根据该模型，从序列中的第二帧到最后一帧都可以实现细胞追踪。实验结果表明，与仅使用拓扑约束追踪相比，这种方法具有更高的精度。序列I和序列II的最终平均追踪精度分别提高了10.17%和4%。
