Fully automated quantification of leaf venation structure

J. Mounsef, Lina Karam

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)

Abstract

Recently, there has been a surge of diverse approaches to investigate leaf vein patterning, covering genetic analyses, pharmacological approaches and theoretical modeling. Genetic and pharmacological approaches remain at this stage insufficient f or analyzing the formation of vascular patterns since the molecular mechanisms involved are still unclear. Similarly, theoretical models are not sufficiently constrained, and thus difficult to validate or disprove. Only few exceptions attempted to provide a link between experimental and theoretical studies by implementing different imaging techniques. Visual imaging methods have been lately extensively used in applications that are targeted to understand and analyze physical biological patterns, specifically to classify different leaf species and quantify leaf venation patterns. There is a rich literature on imaging applications in the above field and various techniques have been developed. However, current methods that aimed to provide high precision results, failed to avoid manual intervention and user assistance for the developed software tools. In this paper, we introduce a fully automated imaging approach for extracting spatial vein pattern data from leaf images, such as vein densities but also vein reticulation (loops) sizes and shapes. We applied this method to quantify leaf venation patterns of the first rosette leaf of Arabidopsis thaliana throughout a series of developmental stages. In particular, we characterized the size and shape of vein network reticulations, which enlarge and get split by new veins as a leaf develops. For this purpose, the approach uses imaging techniques in a fully automatic way that enables the user to batch process a high throughput of data without any manual intervention, yet giving highly accurate results.

Original languageEnglish (US)
Title of host publicationProceedings of the 2012 International Conference on Artificial Intelligence, ICAI 2012
Pages820-825
Number of pages6
Volume2
StatePublished - 2012
Event2012 International Conference on Artificial Intelligence, ICAI 2012 - Las Vegas, NV, United States
Duration: Jul 16 2012Jul 19 2012

Other

Other2012 International Conference on Artificial Intelligence, ICAI 2012
CountryUnited States
CityLas Vegas, NV
Period7/16/127/19/12

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Imaging techniques
Throughput

Keywords

  • Adaptive thresholding
  • Feature extraction
  • Feature quantification
  • Fully automation
  • Leaf venation pattern

ASJC Scopus subject areas

  • Artificial Intelligence

Cite this

Mounsef, J., & Karam, L. (2012). Fully automated quantification of leaf venation structure. In Proceedings of the 2012 International Conference on Artificial Intelligence, ICAI 2012 (Vol. 2, pp. 820-825)

Fully automated quantification of leaf venation structure. / Mounsef, J.; Karam, Lina.

Proceedings of the 2012 International Conference on Artificial Intelligence, ICAI 2012. Vol. 2 2012. p. 820-825.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Mounsef, J & Karam, L 2012, Fully automated quantification of leaf venation structure. in Proceedings of the 2012 International Conference on Artificial Intelligence, ICAI 2012. vol. 2, pp. 820-825, 2012 International Conference on Artificial Intelligence, ICAI 2012, Las Vegas, NV, United States, 7/16/12.
Mounsef J, Karam L. Fully automated quantification of leaf venation structure. In Proceedings of the 2012 International Conference on Artificial Intelligence, ICAI 2012. Vol. 2. 2012. p. 820-825
Mounsef, J. ; Karam, Lina. / Fully automated quantification of leaf venation structure. Proceedings of the 2012 International Conference on Artificial Intelligence, ICAI 2012. Vol. 2 2012. pp. 820-825
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