Purpose: Blood vessel networks within the retina are crucial for maintaining tissue perfusion and therefore good vision. Their complexity and unique patterns often require a steep learning curve for humans to identify trends and changes in the shape and topology of the networks, even though there exists much information important to identifying disease within them.
Methods: Through image processing, the vasculature is isolated from other features of the fundus images, forcing the viewer to focus on the complex vascular feature. This article explores an approach using a grammar based on shape to describe retinal vasculature and to generate realistic and increasingly unrealistic artificial vascular networks that are then reviewed by ophthalmologists via digital survey. The ophthalmologists are asked whether these artificial vascular networks appeared realistic or unrealistic.
Results: With only three rules (initiate, branch, and curve), the grammar accomplishes these goals. Networks are generated by adding noise to rule parameters present in existing networks. Via the survey of synthetic networks generated with different noise parameters, a correlation between noise in the branch rule and realistic association is revealed.
Conclusions: By creating a language to describe retinal vasculature, this article allows for the potential of new insight into such an important but less understood feature of the retina, which in the future may play a role in diagnosing or helping to predict types of ocular disease.
Translational relevance: Applying shape grammar to describe retinal vasculature permits new understanding, which in turn provides the potential for new diagnostic tools.
Keywords: Shape grammar; image processing; retinal image analysis; synthetic vasculature.
Copyright 2020 The Authors.