Title
A multiCell visual tracking algorithm using multi-task particle swarm optimization for low-contrast image sequences.
Abstract
In terms of the varying number of cell population, shape deformation, collision and uneven movement, a novel method based on multi-task particle swarm optimization (PSO) algorithm without explicit detection module, named MTPSO tracking method, is developed for automatic tracking of biological cells in time-lapse low-contrast microscopy image sequences. For tracking existing cells from the previous frames, a PSO-based tracking module is firstly implemented to give the initial positions of existing cells according to the previous estimated state of each cell, then a PSO-based contour module is proposed to determine the corresponding contour of each cell and finally achieve a precise position tracking by an iterative centroid updating process. For tracking new appearing cells at the current frame, a PSO-based discovery module, followed by the aforementioned PSO-based contour module, is proposed to search for new potential cells through appropriate initialization of particle swarm and searching mechanism. MTPSO tracking method is tested over a number of different real cell image sequences and is shown to provide high accuracy both in position and contour estimate of each cell in various challenging cases. Furthermore, it is more competitive against the state-of-the-art multi-object tracking methods in terms of performance measures such as FAR, FNR, LTR, and LSR.
Year
DOI
Venue
2016
https://doi.org/10.1007/s10489-016-0802-2
Appl. Intell.
Keywords
Field
DocType
Multi-cell tracking,Particle Swarm Optimization (PSO),Image processing
Particle swarm optimization,Computer vision,Population,Computer science,Image processing,Algorithm,Collision,Eye tracking,Artificial intelligence,Initialization,Uneven movement,Centroid
Journal
Volume
Issue
ISSN
45
4
0924-669X
Citations 
PageRank 
References 
1
0.35
14
Authors
5
Name
Order
Citations
PageRank
Yayun Ren141.45
Benlian Xu28120.96
Peiyi Zhu321.39
Mingli Lu43611.58
Jiang Dongmei511515.28