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.