Fractal analysis of the retinal vascular network in fundus images

MacGillivray, Thomas J.; Patton, Niall; Doubal, Fergus; Graham, Catriona; Wardlaw, Joanna M.

doi:10.1109/iembs.2007.4353837

Cited by 54 publications

(56 citation statements)

References 16 publications

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“…Fractal analysis has been used in many branches of medicine to characterize the geometric complexity of blood vessels, including those in the eyes (9,12,14). The geometric complexity of the retinal vasculature can be quantified through calculation of fractal dimension from digital retinal images.…”

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confidence: 99%

Quantitative Assessment of Early Diabetic Retinopathy Using Fractal Analysis

et al. 2009

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OBJECTIVE -Fractal analysis can quantify the geometric complexity of the retinal vascular branching pattern and may therefore offer a new method to quantify early diabetic microvascular damage. In this study, we examined the relationship between retinal fractal dimension and retinopathy in young individuals with type 1 diabetes.RESEARCH DESIGN AND METHODS -We conducted a cross-sectional study of 729 patients with type 1 diabetes (aged 12-20 years) who had seven-field stereoscopic retinal photographs taken of both eyes. From these photographs, retinopathy was graded according to the modified Airlie House classification, and fractal dimension was quantified using a computerbased program following a standardized protocol.RESULTS -In this study, 137 patients (18.8%) had diabetic retinopathy signs; of these, 105 had mild retinopathy. Median (interquartile range) retinal fractal dimension was 1.46214 (1.45023-1.47217). After adjustment for age, sex, diabetes duration, A1C, blood pressure, and total cholesterol, increasing retinal vascular fractal dimension was significantly associated with increasing odds of retinopathy (odds ratio 3.92 [95% CI 2.02-7.61] for fourth versus first quartile of fractal dimension). In multivariate analysis, each 0.01 increase in retinal vascular fractal dimension was associated with a nearly 40% increased odds of retinopathy (1.37 [1.21-1.56]). This association remained after additional adjustment for retinal vascular caliber.CONCLUSIONS -Greater retinal fractal dimension, representing increased geometric complexity of the retinal vasculature, is independently associated with early diabetic retinopathy signs in type 1 diabetes. Fractal analysis of fundus photographs may allow quantitative measurement of early diabetic microvascular damage.

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confidence: 99%

Quantitative Assessment of Early Diabetic Retinopathy Using Fractal Analysis

et al. 2009

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“…Examples of artefacts erroneously included in the line tracing are choroid vessels, pigment abnormalities and photograph halos. The retinal vascular fractal dimension was calculated from the refined line tracing by the box-counting method of the program, an established method for structures that are not perfectly self-similar, such as the retinal vasculature [19][20][21]. Figure 1 shows examples of retinal colour images and the corresponding refined line tracing for two of the participants in this study.…”

Section: Methodsmentioning

confidence: 99%

Retinal vascular fractals predict long-term microvascular complications in type 1 diabetes mellitus: the Danish Cohort of Pediatric Diabetes 1987 (DCPD1987)

et al. 2014

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Aims/hypothesis Fractal analysis of the retinal vasculature provides a global measure of the complexity and density of retinal vessels summarised as a single variable: the fractal dimension. We investigated fractal dimensions as long-term predictors of microvasculopathy in type 1 diabetes. Methods We included 180 patients with type 1 diabetes in a 16 year follow-up study. In baseline retinal photographs (from 1995), all vessels in a zone 0.5-2.0 disc diameters from the disc margin were traced using Singapore Institute Vessel Assessment-Fractal image analysis software. Artefacts were removed by a certified grader, and fractal dimensions were calculated using the box-counting method. At follow-up (in 2011), diabetic neuropathy, nephropathy and proliferative retinopathy were assessed and related to baseline fractal dimensions in multiple regressions adjusted for sex and baseline age, diabetes duration, HbA 1c , BP, BMI, vibration perception threshold, albuminuria, retinopathy and vessel diameters. Results Mean baseline age and diabetes duration were 21.0 and 13.4 years, respectively, and of patients 50.0% were males. The mean fractal dimension was 1.3817. The 16 year incidences of neuropathy, nephropathy and proliferative retinopathy were 10.8%, 8.0% and 27.9%, respectively. Multiple regression analyses showed a lower fractal dimension to significantly predict incident neuropathy (OR 1.17 per 0.01 fractal dimension decrease [95% CI 1.01, 1.36]), nephropathy (OR 1.40 per 0.01 fractal dimension decrease [95% CI 1.10, 1.79]) and proliferative retinopathy (OR 1.22 per 0.01 fractal dimension decrease [95% CI 1.09, 1.37]). Conclusions/interpretation The retinal vascular fractal dimension is a shared biomarker of diabetic microvasculopathy, thus indicating a possible common pathogenic pathway. Retinal fractal analysis therefore is a potential tool for risk stratification in type 1 diabetes.

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“…The advantage of FFD is that it computes the fractal dimension of gray scale images, and eliminates the need for image segmentation [31]. It has also been found to be relatively insensitive to noise and it is believed to work effectively with data having low signal-to-noise ratio [31,32]. We adapted the similar FFD approach proposed by Azemin M. and et al [30].…”

Section: ) Fractal Analysismentioning

confidence: 99%

“…The parameters related such as slope and intercept are generated based on this approach. Finally we applied the multifractal spectra since it can describe the evolution of the probability distribution of fractal structures [28,[31][32][33]. Instead of using simple fractals (or monofractals), multifractals are characterized by a hierarchy of exponents, rather than a single fractal dimension.…”

Section: ) Fractal Analysismentioning

confidence: 99%

Segmentation and texture analysis with multimodel inference for the automatic detection of exudates in early diabetic retinopathy

Lee¹,

Zee²,

Li³

2013

JBiSE

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Diabetic retinopathy (DR) is an eye disease caused by the increase of insulin in blood and may cause blindness if not treated at an early stage. Exudates are the primary sign of DR. Currently there is no fully automated method to detect exudates in the literature and it would be useful in large scale screening if fully automatic method is available. In this paper we developed a novel method to detect exudates that based on interactions between texture analysis and segmentation with mathematical morphological technique by using multimodel inference. The texture analysis involves three components: they are statistical texture analysis, high order spectra analysis, and fractal analysis. The performance of the proposed method is assessed by the sensitivity, specificity and accuracy using the public data DIARETDB1. Our results show that the sensitivity, specificity and accuracy are 95.7%, 97.6% and 98.7% (SE = 0.01), respectively. It is shown that the proposed method can be run automatically and also improve the accuracy of exudates detection significantly over most of the previous methods.

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Fractal analysis of the retinal vascular network in fundus images

Cited by 54 publications

References 16 publications

Quantitative Assessment of Early Diabetic Retinopathy Using Fractal Analysis

Quantitative Assessment of Early Diabetic Retinopathy Using Fractal Analysis

Retinal vascular fractals predict long-term microvascular complications in type 1 diabetes mellitus: the Danish Cohort of Pediatric Diabetes 1987 (DCPD1987)

Segmentation and texture analysis with multimodel inference for the automatic detection of exudates in early diabetic retinopathy

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