拓扑约束结合匈牙利算法在高密度神经干细胞追踪中的研究

Tracking of Neural Stem Cells in High Density Image Sequence Based on Topological Constraint Combined with Hungarian Algorithm

神经干细胞(NSCs)的运动分析是细胞学和生物学研究中重要的组成部分之一,而对大量NSCs同时进行追踪是细胞运动研究的主要难点。为了进一步提高高密度NSCs追踪算法的准确性,本文提出了一种新的基于分割、结合拓扑约束和数据关联的细胞追踪方法。首先针对实验所用的两组细胞图像序列的特点,分别采用了不同的分割方法。然后利用拓扑约束完成相邻两帧中所有细胞的数据关联并建立系数矩阵,最后对该系数矩阵利用匈牙利算法实现细胞的最优匹配,以此模式从序列的前两帧到最后一帧完成细胞追踪。实验结果表明,本文算法与单独利用拓扑约束进行细胞追踪的方法相比,有更好的追踪效果,准确性更高,序列I和序列Ⅱ的最终追踪准确率分别提高了10.17%和4%。

Analysis of neural stem cells＇ movements is one of the important parts in the fields of cellular and biologi- cal research. The main difficulty existing in cells＇ movement study is whether the cells tracking system can simulta- neously track and analyze thousands of neural stem cells （NSCs） automatically. We present a novel cells＇ tracking al- gorithm 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 characteris- tics of the two cell image sequences in our experiment. Then we formed a data association and constituted a coeffi- cient 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 1I have been improved 10. 17 ~ and 4 ~, respectively.