Result: Multiple particle tracking for live cell imaging with green fluorescent protein (GFP) tagged videos
Title:
Multiple particle tracking for live cell imaging with green fluorescent protein (GFP) tagged videos
Authors:
Source:
Pattern recognition and data mining (Bath, 22-25 august 2005)Lecture notes in computer science.
Publisher Information:
Berlin: Springer, 2005.
Publication Year:
2005
Physical Description:
print, 13 ref 2
Original Material:
INIST-CNRS
Subject Terms:
Computer science, Informatique, Sciences exactes et technologie, Exact sciences and technology, Sciences appliquees, Applied sciences, Informatique; automatique theorique; systemes, Computer science; control theory; systems, Logiciel, Software, Organisation des mémoires. Traitement des données, Memory organisation. Data processing, Traitement des données. Listes et chaînes de caractères, Data processing. List processing. Character string processing, Intelligence artificielle, Artificial intelligence, Reconnaissance des formes. Traitement numérique des images. Géométrie algorithmique, Pattern recognition. Digital image processing. Computational geometry, Agent mobile, Mobile agent, Agente movil, Algorithme adaptatif, Adaptive algorithm, Algoritmo adaptativo, Fonction Green, Green function, Función Green, Formation image, Imaging, Formación imagen, Fouille donnée, Data mining, Busca dato, Microscopie confocale, Confocal microscopy, Microscopía confocal, Orienté objet, Object oriented, Orientado objeto, Pistage, Tracking, Rastreo, Poursuite cible, Target tracking, Protéine, Protein, Proteína, Reconnaissance forme, Pattern recognition, Reconocimiento patrón, Segmentation image, Image segmentation, Signal vidéo, Video signal, Señal video, Traitement image, Image processing, Procesamiento imagen, Algorithme cellulaire, Cellular algorithm, Algoritmo celular
Document Type:
Conference
Conference Paper
File Description:
text
Language:
English
Author Affiliations:
ATR Labs, Research School of Informatics, Loughborough University, United Kingdom
Dept. of Biochemistry, School of Medical Sciences, University of Bristol, United Kingdom
Dept. of Biochemistry, School of Medical Sciences, University of Bristol, United Kingdom
ISSN:
0302-9743
Rights:
Copyright 2005 INIST-CNRS
CC BY 4.0
Sauf mention contraire ci-dessus, le contenu de cette notice bibliographique peut être utilisé dans le cadre d’une licence CC BY 4.0 Inist-CNRS / Unless otherwise stated above, the content of this bibliographic record may be used under a CC BY 4.0 licence by Inist-CNRS / A menos que se haya señalado antes, el contenido de este registro bibliográfico puede ser utilizado al amparo de una licencia CC BY 4.0 Inist-CNRS
CC BY 4.0
Sauf mention contraire ci-dessus, le contenu de cette notice bibliographique peut être utilisé dans le cadre d’une licence CC BY 4.0 Inist-CNRS / Unless otherwise stated above, the content of this bibliographic record may be used under a CC BY 4.0 licence by Inist-CNRS / A menos que se haya señalado antes, el contenido de este registro bibliográfico puede ser utilizado al amparo de una licencia CC BY 4.0 Inist-CNRS
Notes:
Computer science; theoretical automation; systems
Accession Number:
edscal.17094900
Database:
PASCAL Archive
Further Information
Particle tracking is important for understanding the mobile behaviour of objects of varying sizes in a range of physical and biological science applications. In this paper we present a new algorithm for tracking cellular particles imaged using a confocal microscope. The algorithm performs adaptive image segmentation to identify objects for tracking and uses intelligent estimates of neighbourhood search, spatial relationship, velocity, direction estimates, and shape/size estimates to perform robust tracking. Our tracker is tested on three videos for vesicle tracking in GFP tagged videos. The results are compared to the popular Harvard tracker and we show that our tracking scheme offers better performance and flexibility for tracking.