However, such measurements are complicated by varying refractive indices and the effects of magnification of the optic media, requiring the use of corrective factors which may be inaccurate.Īn alternative way of quantitatively describing the retinal vascular network is to summarise the geometry of individual bifurcations in terms of their bifurcation angles (ω) and junction exponents (X) (see Fig 1). More recently, quantitative assessments of the retinal vasculature have been performed.
Although these changes have prognostic value, most hypertensives now present early with minimal retinal change and qualitative assessment in these circumstances is of little prognostic use. Changes in the hypertensive retina may be qualitatively graded according to the presence of haemorrhages, exudates, and arteriovenous crossing phenomena. The retinal arteriolar network is readily viewed in vivo and has long been known to exhibit abnormalities in disease states such as hypertension and diabetes. Application of automated methods of retinal vascular analysis may be useful in the assessment of cardiovascular and other diseases. The Sobel method was the least consistent owing to frequent misinterpretation of the light reflex as the vessel edge.ĬONCLUSION Of the three automated methods compared, the SLRF method was the most consistent (defined as the method producing diameter estimations with the least scatter) and the most repeatable in measurements of retinal arteriolar diameter. The SLRF method was the most repeatable and the Gaussian method less so. RESULTS Diameter estimations obtained using the SLRF method were the least scattered although diameters obtained were approximately 3 pixels greater than those measured manually. Method agreement was analysed using Bland–Altman plots and the repeatability of each method was assessed.
COMPUTER ALGORITHMS FOR STATISTICAL CALCULATIONS MANUAL
METHODS 60 diameters (in pixels) measured by manual identification of vessel edges in red-free retinal images were compared with diameters measured by (1) fitting vessel intensity profiles to a double Gaussian function by non-linear regression, (2) a standard edge detection algorithm (Sobel), and (3) determination of points of maximum intensity variation by a sliding linear regression filter (SLRF). Three automated methods and a manual method for measurement of arteriolar diameters from digitised red-free retinal photographs were compared. These problems may be overcome using computer algorithms.
AIMS Quantification of retinal vascular change is difficult and manual measurements of vascular features are slow and subject to observer bias.
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