Multi-task ant system for multi-object parameter estimation and its application in cell tracking

•The multi-task ant system, is first introduced to track multiple objects.•Our method can track multiple cells in various challenging scenarios.•A novel likelihood function is developed to find the cell potentials.•Our algorithm enjoys a robust tracking performance with low FNR and FAR. Inspired by...

Full description

Saved in:
Bibliographic Details
Published inApplied soft computing Vol. 35; pp. 449 - 469
Main Authors Xu, Benlian, Lu, Mingli, Ren, Yayun, Zhu, Peiyi, Shi, Jian, Cheng, Dahai
Format Journal Article
LanguageEnglish
Published Elsevier B.V 01.10.2015
Subjects
Ant system
Cell tracking
Parameter estimation
Ant system
Cell tracking
Parameter estimation
Online AccessGet full text

Cover

More Information
Summary:•The multi-task ant system, is first introduced to track multiple objects.•Our method can track multiple cells in various challenging scenarios.•A novel likelihood function is developed to find the cell potentials.•Our algorithm enjoys a robust tracking performance with low FNR and FAR. Inspired by ant's stochastic behavior in search for multiple food sources, we propose a cooperating multi-task ant system for tracking multiple synthetic objects as well as multiple real cells in a bio-medical field. In our framework, each ant colony is assumed and assigned to fulfill a given task to estimate the state of an object. Furthermore, two ant levels are used, i.e., ant individual level and ant cooperation level. In the ant individual level, ants within one colony perform independently, and the motion of each individual is probabilistically determined by both its intended motion modes and the likelihood function score. In the ant cooperation level, each ant adjusts individual state within its influence region according to heuristic information of all other ants within the same colony, while the global best template at current iteration is found among all ant colonies and utilized to update ant model probability, influence region, and probability of fulfilling task. Our algorithm is validated by comparing it to the-state-of-art algorithms, and specifically the improved tracking performance in terms of false negative rate (up to 10.0%) and false negative rate (up to 2.1%) is achieved based on the studied three real cell image sequences.
ISSN:1568-4946
1872-9681
DOI:10.1016/j.asoc.2015.06.045