title: Micro-object motion tracking based on the probability hypothesis density particle tracker.
abstract: 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.
authors.affiliation: Department of Mathematics, Harbin Institute of Technology, Harbin, 150001, China. shichunmei1210@gmail.com.
authors.forename: Chunmei
authors.initials: C
authors.lastname: Shi
authors.affiliation: School of Computer Science and Technology, Harbin Institute of Technology, Harbin, 150001, China.
authors.forename: Lingling
authors.initials: L
authors.lastname: Zhao
authors.affiliation: School of Computer Science and Technology, Harbin Institute of Technology, Harbin, 150001, China.
authors.forename: Junjie
authors.initials: J
authors.lastname: Wang
authors.affiliation: Department of Mathematics, Harbin Institute of Technology, Harbin, 150001, China.
authors.forename: Chiping
authors.initials: C
authors.lastname: Zhang
authors.affiliation: School of Computer Science and Technology, Harbin Institute of Technology, Harbin, 150001, China.
authors.forename: Xiaohong
authors.initials: X
authors.lastname: Su
authors.affiliation: School of Computer Science and Technology, Harbin Institute of Technology, Harbin, 150001, China.
authors.forename: Peijun
authors.initials: P
authors.lastname: Ma
mesh_terms: D000818:Animals; D001499:Bayes Theorem; D002465:Cell Movement; D003198:Computer Simulation; D016013:Likelihood Functions; D055641:Mathematical Concepts; D051379:Mice; D008853:Microscopy; D008870:Microtubules / Q000502:physiology / Q000648:ultrastructure; D008954:Models, Biological; D009068:Movement; D010363:Pattern Recognition, Automated / Q000379:methods* / Q000706:statistics & numerical data; D011336:Probability; D013234:Stem Cells / Q000166:cytology / Q000502:physiology
keywords: Micro-objects tracking; Microscopic image sequences; Probability hypothesis density particle filtering (PF-PHD) tracker; Track continuity