Automated analysis of leaf venation patterns
Visual imaging methods have been lately extensively used in applications that are targeted to understand and analyze botanical patterns. There is a rich literature on imaging applications in the above field and various techniques have been developed. 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 mainly uses skeletonization along with other known imaging techniques in an automatic interactive way that enables the user to batch process a high throughput of data.