Evaluation of fractal dimension effectiveness for damage detection in retinal background

Colomer, Adrián; Naranjo, Valery; Janvier, Thomas; Mossi, José M.

doi:10.1016/j.cam.2018.01.005

Cited by 13 publications

(7 citation statements)

References 36 publications

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“…There have been other similar longitudinal studies that found no evidence of an association [ 33 ], and one study found an association between higher arteriolar fractal dimension and diabetic retinopathy [ 35 ]. Using different analysis and computational methods, two recent studies showed the ability of fractal dimension to assist in identification of very early retinal disease [ 36 , 37 ]. Both studies were conducted in people with retinopathy, so prediction was not the aim.…”

Section: Discussionmentioning

confidence: 99%

Retinal venular tortuosity and fractal dimension predict incident retinopathy in adults with type 2 diabetes: the Edinburgh Type 2 Diabetes Study

Forster

Garcia²,

Sluiman

et al. 2021

Diabetologia

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Aims/hypothesis Our aim was to determine whether a range of prespecified retinal vessel traits were associated with incident diabetic retinopathy in adults with type 2 diabetes. Methods In the prospective observational cohort Edinburgh Type 2 Diabetes Study of 1066 adults with type 2 diabetes, aged 60–75 years at recruitment, 718 were free from diabetic retinopathy at baseline. Baseline retinal traits including vessel widths, tortuosity (curvature) and fractal dimensions (network complexity), were quantified using fundus camera images and semiautomated software, and analysed using logistic regression for their association with incident diabetic retinopathy over 10 years. Results The incidence of diabetic retinopathy was 11.4% (n = 82) over 10 years. After adjustment for a range of vascular and diabetes-related risk factors, both increased venular tortuosity (OR 1.51; 95% CI 1.15, 1.98; p = 0.003) and decreased fractal dimension (OR 0.75; 95% CI 0.58, 0.96; p = 0.025) were associated with incident retinopathy. There was no evidence of an association with arterial tortuosity, and associations between measurements of vessel widths and retinopathy lost statistical significance after adjustment for diabetes-related factors and vascular disease. Adding venular tortuosity to a model including established risk factors for diabetic retinopathy (HbA1c, BP and kidney function) improved the discriminative ability (C statistic increased from 0.624 to 0.640, p = 0.013), but no such benefit was found with fractal dimension. Conclusions/interpretation Increased retinal venular tortuosity and decreased fractal dimension are associated with incident diabetic retinopathy, independent of classical risk factors. There is some evidence that venular tortuosity may be a useful biomarker to improve the predictive ability of models based on established retinopathy risk factors, and its inclusion in further risk prediction modelling is warranted. Graphical abstract

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Section: Discussionmentioning

confidence: 99%

Retinal venular tortuosity and fractal dimension predict incident retinopathy in adults with type 2 diabetes: the Edinburgh Type 2 Diabetes Study

Forster

Garcia²,

Sluiman

et al. 2021

Diabetologia

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“…The multifractal nature of the vascular network of the human retina [36, 37] and the reduction of the vasculature complexity with aging [38] have been reported. Fractal analysis has also been used to differentiate between healthy and pathological retinal texture [39].…”

Section: Introductionmentioning

confidence: 99%

Network-based features for retinal fundus vessel structure analysis

et al. 2019

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Retinal fundus imaging is a non-invasive method that allows visualizing the structure of the blood vessels in the retina whose features may indicate the presence of diseases such as diabetic retinopathy (DR) and glaucoma. Here we present a novel method to analyze and quantify changes in the retinal blood vessel structure in patients diagnosed with glaucoma or with DR. First, we use an automatic unsupervised segmentation algorithm to extract a tree-like graph from the retina blood vessel structure. The nodes of the graph represent branching (bifurcation) points and endpoints, while the links represent vessel segments that connect the nodes. Then, we quantify structural differences between the graphs extracted from the groups of healthy and non-healthy patients. We also use fractal analysis to characterize the extracted graphs. Applying these techniques to three retina fundus image databases we find significant differences between the healthy and non-healthy groups (p-values lower than 0.005 or 0.001 depending on the method and on the database). The results are sensitive to the segmentation method (manual or automatic) and to the resolution of the images.

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“…The experimental results of proposed approach are compared with nine existing approaches such as Shabbir et al (), Akram et al (), Kusakunniran et al (), Colomer, Naranjo, Janvier, and Mossi (), Issac et al (), Dashtbozorg, Zhang, Huang, and ter Haar Romeny (), Acharya et al (), and Kaur and Mittal (). Gabor wavelets and Otsu method are applied only for detection of soft‐exudates.…”

Section: Resultsmentioning

confidence: 99%

“…It obtains 95.5% ACC (Akram et al, ). SVM is applied for retinal lesion detection (Colomer et al, ; Issac et al). The supervised based learning method is used for the retinal lesion.…”

Section: Resultsmentioning

confidence: 99%

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Diabetic retinopathy detection and classification using hybrid feature set

Amin

Sharif

Rehman

et al. 2018

Microscopy Res & Technique

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Complicated stages of diabetes are the major cause of Diabetic Retinopathy (DR) and no symptoms appear at the initial stage of DR. At the early stage diagnosis of DR, screening and treatment may reduce vision harm. In this work, an automated technique is applied for detection and classification of DR. A local contrast enhancement method is used on grayscale images to enhance the region of interest. An adaptive threshold method with mathematical morphology is used for the accurate lesions region segmentation. After that, the geometrical and statistical features are fused for better classification. The proposed method is validated on DIARETDB1, E‐ophtha, Messidor, and local data sets with different metrics such as area under the curve (AUC) and accuracy (ACC).

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Evaluation of fractal dimension effectiveness for damage detection in retinal background

Cited by 13 publications

References 36 publications

Retinal venular tortuosity and fractal dimension predict incident retinopathy in adults with type 2 diabetes: the Edinburgh Type 2 Diabetes Study

Retinal venular tortuosity and fractal dimension predict incident retinopathy in adults with type 2 diabetes: the Edinburgh Type 2 Diabetes Study

Network-based features for retinal fundus vessel structure analysis

Diabetic retinopathy detection and classification using hybrid feature set

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