  Introduction: Blood vessels can be non-invasively visualized from a digital
fundus image (DFI). Several studies have shown an association between
cardiovascular risk and vascular features obtained from DFI. Recent advances in
computer vision and image segmentation enable automatising DFI blood vessel
segmentation. There is a need for a resource that can automatically compute
digital vasculature biomarkers (VBM) from these segmented DFI. Methods: In this
paper, we introduce a Python Vasculature BioMarker toolbox, denoted PVBM. A
total of 11 VBMs were implemented. In particular, we introduce new algorithmic
methods to estimate tortuosity and branching angles. Using PVBM, and as a proof
of usability, we analyze geometric vascular differences between glaucomatous
patients and healthy controls. Results: We built a fully automated vasculature
biomarker toolbox based on DFI segmentations and provided a proof of usability
to characterize the vascular changes in glaucoma. For arterioles and venules,
all biomarkers were significant and lower in glaucoma patients compared to
healthy controls except for tortuosity, venular singularity length and venular
branching angles.
  Conclusion: We have automated the computation of 11 VBMs from retinal blood
vessel segmentation. The PVBM toolbox is made open source under a GNU GPL 3
license and is available on physiozoo.com (following publication).
