Bedoukian   RussellIPM   RussellIPM   Piezoelectric Micro-Sprayer


Home
Animal Taxa
Plant Taxa
Semiochemicals
Floral Compounds
Semiochemical Detail
Semiochemicals & Taxa
Synthesis
Control
Invasive spp.
References

Abstract

Guide

Alphascents
Pherobio
InsectScience
E-Econex
Counterpart-Semiochemicals
Print
Email to a Friend
Kindly Donate for The Pherobase

« Previous AbstractAir quality inside subway metro indoor environment worldwide: A review    Next AbstractMachine Learning Analysis of Electronic Nose in a Transdiagnostic Community Sample With a Streamlined Data Collection Approach: No Links Between Volatile Organic Compounds and Psychiatric Symptoms »

IEEE J Biomed Health Inform


Title:An Ant Colony Inspired Multi-Bernoulli Filter for Cell Tracking in Time-Lapse Microscopy Sequences
Author(s):Xu B; Lu M; Cong J; Nener BD;
Address:
Journal Title:IEEE J Biomed Health Inform
Year:2020
Volume:20191028
Issue:6
Page Number:1703 - 1716
DOI: 10.1109/JBHI.2019.2949976
ISSN/ISBN:2168-2208 (Electronic) 2168-2194 (Linking)
Abstract:"The analysis of the dynamic behavior of cells in time-lapse microscopy sequences requires the development of reliable and automatic tracking methods capable of estimating individual cell states and delineating the lineage trees corresponding to the tracks. In this paper, we propose a novel approach, i.e., an ant colony inspired multi-Bernoulli filter, to handle the tracking of a collection of cells within which mitosis, morphological change and erratic dynamics occur. The proposed technique treats each ant colony as an independent one in an ant society, and the existence probability of an ant colony and its density distribution approximation are derived from the individual pheromone field and the corresponding heuristic information for the approximation to the multi-Bernoulli parameters. To effectively guide ant foraging between consecutive frames, a dual prediction mechanism is proposed for the ant colony and its pheromone field. The algorithm performance is tested on challenging datasets with varying population density, frequent cell mitosis and uneven motion over time, demonstrating that the algorithm outperforms recently reported approaches"
Keywords:"*Algorithms Cell Line Cell Movement/physiology Cell Tracking/*methods Humans Microscopy/*methods Mitosis/physiology Models, Biological Time-Lapse Imaging/*methods;"
Notes:"MedlineXu, Benlian Lu, Mingli Cong, Jinliang Nener, Brett D eng Research Support, Non-U.S. Gov't 2019/11/02 IEEE J Biomed Health Inform. 2020 Jun; 24(6):1703-1716. doi: 10.1109/JBHI.2019.2949976. Epub 2019 Oct 28"

 
Back to top
 
Citation: El-Sayed AM 2024. The Pherobase: Database of Pheromones and Semiochemicals. <http://www.pherobase.com>.
© 2003-2024 The Pherobase - Extensive Database of Pheromones and Semiochemicals. Ashraf M. El-Sayed.
Page created on 22-11-2024