Micro-object motion tracking based on the probability hypothesis density particle tracker
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- 作者：Chunmei Shi ; Lingling Zhao ; Junjie Wang ; Chiping Zhang…
- 关键词：Microscopic image sequences ; Micro ; objects tracking ; Probability hypothesis density particle filtering (PF ; PHD) tracker ; Track continuity
- 刊名：Journal of Mathematical Biology
- 出版年：2016
- 出版时间：April 2016
- 年：2016
- 卷：72
- 期：5
- 页码：1225-1254
- 全文大小：1,363 KB
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Lingling Zhao (2)
Junjie Wang (2)
Chiping Zhang (1)
Xiaohong Su (2)
Peijun Ma (2)
1. Department of Mathematics, Harbin Institute of Technology, Harbin, 150001, China
2. School of Computer Science and Technology, Harbin Institute of Technology, Harbin, 150001, China - 刊物类别：Mathematics and Statistics
- 刊物主题：Mathematics
Mathematical Biology
Applications of Mathematics - 出版者：Springer Berlin / Heidelberg
- ISSN：1432-1416
文摘
Tracking micro-objects in the noisy microscopy image sequences is important for the analysis of dynamic processes in biological objects. In this paper, an automated tracking framework is proposed to extract the trajectories of micro-objects. This framework uses a probability hypothesis density particle filtering (PF-PHD) tracker to implement a recursive state estimation and trajectories association. In order to increase the efficiency of this approach, an elliptical target model is presented to describe the micro-objects using shape parameters instead of point-like targets which may cause inaccurate tracking. A novel likelihood function, not only covering the spatiotemporal distance but also dealing with geometric shape function based on the Mahalanobis norm, is proposed to improve the accuracy of particle weight in the update process of the PF-PHD tracker. Using this framework, a larger number of tracks are obtained. The experiments are performed on simulated data of microtubule movements and real mouse stem cells. We compare the PF-PHD tracker with the nearest neighbor method and the multiple hypothesis tracking method. Our PF-PHD tracker can simultaneously track hundreds of micro-objects in the microscopy image sequence.