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

• Published in: Current eye research Vol. 38; no. 7; pp. 781 - 792
• Main Author: lu, tefan
• Format: Journal Article
• Language: English
• Published: England Informa Healthcare USA, Inc 01.07.2013; Taylor & Francis
• 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
• ISSN: 0271-3683; 1460-2202; 1460-2202
• DOI: 10.3109/02713683.2013.779722

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.