Fractal Characterization of Retinal Microvascular Network Morphology During Diabetic Retinopathy Progression

ABSTRACT Objective: The study aimed to characterize morphological changes of the retinal microvascular
network during the progression of diabetic retinopathy.

Methods: Publicly available retinal images captured by a digital fundus camera from DIARETDB1 and
STARE databases were used. The retinal microvessels were segmented using the automatic method
and vascular network morphology was analyzed by fractal parametrization such as box-counting
dimension, lacunarity, and multifractals.

Results: The results of the analysis were affected by the ability of the segmentation method to include
smaller vessels with more branching generations. In cases where the segmentation was more detailed
and included a higher number of vessel branching generations, increased severity of diabetic
retinopathy was associated with increased complexity of microvascular network as measured by box-
counting and multifractal dimensions, and decreased gappiness of retinal microvascular network as
measured by lacunarity parameter. This association was not observed if the segmentation method
included only 3-4 vessel branching generations.

Conclusions: Severe stages of diabetic retinopathy could be detected non-invasively by using high
resolution fundus photography and automatic microvascular segmentation to the high number of
branching generations, followed by fractal analysis parametrization. This approach could improve risk
stratification for the development of microvascular complications, cardiovascular disease and
dementia in diabetes.