Multifractal Geometry in Analysis and Processing of Digital Retinal Photographs for Early Diagnosis of Human Diabetic Macular Edema

Abstract Objective: The purpose of this paper is to determine a quantitative assessment of the human retinal vascular network architecture for patients with diabetic macular edema (DME). Multifractal geometry and lacunarity parameters are used in this study. Materials and methods: A set of 10 segmen...

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Bibliographic Details
Published in:Current eye research 2013-07, Vol.38 (7), p.781-792
Main Author: lu, tefan
Format: Article
Language:English
Subjects:
Adult
Aged
Aged, 80 and over
Diabetic macular edema
Diabetic Retinopathy - diagnosis
Early Diagnosis
Fractals
Humans
Image Interpretation, Computer-Assisted
Image Processing, Computer-Assisted
lacunarity
Macular Edema - diagnosis
Middle Aged
multifractals
Predictive Value of Tests
retinal image analysis
retinal microcirculation
Retinal Vessels - pathology
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Summary:Abstract Objective: The purpose of this paper is to determine a quantitative assessment of the human retinal vascular network architecture for patients with diabetic macular edema (DME). Multifractal geometry and lacunarity parameters are used in this study. Materials and methods: A set of 10 segmented and skeletonized human retinal images, corresponding to both normal (five images) and DME states of the retina (five images), from the DRIVE database was analyzed using the Image J software. Statistical analyses were performed using Microsoft Office Excel 2003 and GraphPad InStat software. Results: The human retinal vascular network architecture has a multifractal geometry. The average of generalized dimensions (Dq) for q = 0, 1, 2 of the normal images (segmented versions), is similar to the DME cases (segmented versions). The average of generalized dimensions (Dq) for q = 0, 1 of the normal images (skeletonized versions), is slightly greater than the DME cases (skeletonized versions). However, the average of D2 for the normal images (skeletonized versions) is similar to the DME images. The average of lacunarity parameter, Λ, for the normal images (segmented and skeletonized versions) is slightly lower than the corresponding values for DME images (segmented and skeletonized versions). Conclusion: The multifractal and lacunarity analysis provides a non-invasive predictive complementary tool for an early diagnosis of patients with DME.
ISSN:0271-3683
1460-2202
DOI:10.3109/02713683.2013.779722