NDLI: Tissue-level segmentation and tracking of cells in growing plant roots

Content Provider	Springer Nature Link
Author	Sethuraman, Vijaya French, Andrew Wells, Darren Kebi, Kim Pridmore, Tony
Copyright Year	2011
Abstract	With the spread of systems approaches to biological research, there is increasing demand for methods and tools capable of extracting quantitative measurements of biological samples from individual and time-based sequences of microscope images. To this end, we have developed a software tool for tissue level segmentation and automatic tracking of a network of cells in confocal images of the roots of the model plant Arabidopsis thaliana. The tool implements a novel hybrid technique, which is a combination of the recently developed Network Snakes technique and MCMC-based particle filters and incorporates automatic initialisation of the network snakes. A novel method of evaluation of network-structured multi-target tracking is also presented, and is used to evaluate the developed tracking framework for accuracy and robustness against several timelapse sequences of Arabidopsis roots. Evaluation results are presented, along with a comparison between the results of the component techniques and the hybrid approach. The results show that the hybrid approach performed consistently well at all levels of complexity and better than the component methods alone.
Starting Page	639
Ending Page	658
Page Count	20
File Format	PDF
ISSN	09328092
Journal	Machine Vision and Applications
Volume Number	23
Issue Number	4
e-ISSN	14321769
Language	English
Publisher	Springer-Verlag
Publisher Date	2011-03-25
Publisher Place	Berlin/Heidelberg
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Cells Confocal images Tracking Network snakes Markov Chain Monte Carlo Image Processing and Computer Vision Pattern Recognition Communications Engineering, Networks
Content Type	Text
Resource Type	Article
Subject	Computer Vision and Pattern Recognition Computer Science Applications Software Hardware and Architecture

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