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 noninvasively 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. K E Y W O R D Sdiabetic retinopathy, fractal analysis, lacunarity, microvascular network morphology, multifractals 2 of 12 | POPOVIC et al.

TREND database: Retinal images of healthy young subjects visualized by a portable digital non-mydriatic fundus camera

“…To define the scaling features in the multifractal analysis, the minimum box size was set to 10 pixels and maximum box size to 60%. Multifractal analysis was used to determine the capacity dimension (D 0 ), information dimension (D 1 ) and correlation dimension (D 2 ) as previously explained [4].…”

“…There is an increasing body of research data showing that microvascular changes in the eye reflect changes in microvascular networks in the rest of the body [1,[3][4][5][6][7]. In most of these studies data from only one eye is used to extrapolate these conclusions.…”

“…More recently, retinal microvascular network patterns have also been studied extensively for their use in early detection of ocular and systemic diseases, as well as physiologic changes associated with normal aging [1,2]. For example, decreased retinal microvascular complexity has been found in hypertension and diabetes [3][4][5][6]. Certain regional patterns of decreased retinal vascular complexity measured by multifractal and lacunarity analysis have been found to correlate with cognitive impairment in the aging population [7].…”

A Novel Approach of Diabetic Retinopathy Early Detection Based on Multifractal Geometry Analysis for OCTA Macular Images Using Support Vector Machine

“…Multifractal analysis attracted the attention of many researchers in the field of medical diagnosis. In [30], the retinal microvascular networks morphological changes were characterized by diabetic retinopathy progression stages. Microvascular network in high-resolution fundus photography were segmented to a high number of branching generations followed by fractal analysis parameterization.…”

Regional Patterns in Retinal Microvascular Network Geometry in Health and Disease

“…Although the remodelling of retinal microvascular network in pathologic conditions affects retina globally, the differences in the extent and type of remodelling among various segments of retinal microvascular network may exist 4,5 . These differences in remodelling may stem from the distinct function and histological structure among microvessels of various size 6 .…”