Authors:
Kuan Yan
1
;
Fons Verbeek
1
;
Sylvia Le Dévédec
2
and
Bob van de Water
2
Affiliations:
1
Section Imaging and Bioinformatics, Leiden Institute of Advanced Computer Science, Leiden University, Netherlands
;
2
Section Toxicology, Leiden/Amsterdam Center for Drug Research, Leiden University, Netherlands
Keyword(s):
Object tracking, Cell tracking, Cellular phenotype, Tumour cell, Time-lapse video, Cell migration analysis, KDE mean shift, Steepest descent, High-throughput, High-content, Video analysis, Image sequence.
Related
Ontology
Subjects/Areas/Topics:
Applications
;
Computer Vision, Visualization and Computer Graphics
;
Human-Computer Interaction
;
Image and Video Analysis
;
Methodologies and Methods
;
Model-Based Object Tracking in Image Sequences
;
Motion and Tracking
;
Motion, Tracking and Stereo Vision
;
Pattern Recognition
;
Physiological Computing Systems
;
Software Engineering
;
Video Analysis
Abstract:
In this paper, we address the problem of the analysis of cellular phenotype from time-lapse image sequences using object tracking algorithms and feature extraction and classification. We discusses the application of an object tracking algorithm for in the analysis of high content cell-migration time-lapse image sequence of extremely motile cells; these cells are captured at low time-resolution.. The small size of the objects and significant deformation of the object during the process renders the tracking as a non-trivial problem. To that end, the ‘KDE Mean Shift’, a real-time tracking solution, is adapted for our research. We illustrate that in a simulation experiment with artificial objects, with our algorithm an accuracy of over 90% can be established. Based on the tracking result, we propose several morphology and motility based measurements for the analysis of cell behaviour. Our analysis requires only initial manual interference; the majority of the processing is automated.