TY - GEN
T1 - Fully automated quantification of leaf venation structure
AU - Mounsef, J.
AU - Karam, Lina
PY - 2012/12/1
Y1 - 2012/12/1
N2 - 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.
AB - 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.
KW - Adaptive thresholding
KW - Feature extraction
KW - Feature quantification
KW - Fully automation
KW - Leaf venation pattern
UR - http://www.scopus.com/inward/record.url?scp=84875124638&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84875124638&partnerID=8YFLogxK
M3 - Conference contribution
AN - SCOPUS:84875124638
SN - 1601322178
SN - 9781601322173
T3 - Proceedings of the 2012 International Conference on Artificial Intelligence, ICAI 2012
SP - 820
EP - 825
BT - Proceedings of the 2012 International Conference on Artificial Intelligence, ICAI 2012
T2 - 2012 International Conference on Artificial Intelligence, ICAI 2012
Y2 - 16 July 2012 through 19 July 2012
ER -