Computer algorithms for the automated measurement of retinal arteriolar diameters
- N Chapman1001,
- N Witt10011002,
- X Gao1001,
- A A Bharath1002,
- A V Stanton1001,
- S A Thom1001 and
- A D Hughes1001
- 1001Department of Clinical Pharmacology, School of Medicine at NHLI, Imperial College of Science, Technology and Medicine, St Mary's Hospital, London W2 1NY, UK, 1002Department of Biological and Medical Systems, The Bagrit Centre, Imperial College of Science, Technology and Medicine, London SW7 2BX, UK
- Dr N Chapmann.chapman{at}ic.ac.uk
- Accepted 18 May 2000
Abstract
AIMS Quantification of retinal vascular change is difficult and manual measurements of vascular features are slow and subject to observer bias. These problems may be overcome using computer algorithms. Three automated methods and a manual method for measurement of arteriolar diameters from digitised red-free retinal photographs were compared.
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). Method agreement was analysed using Bland–Altman plots and the repeatability of each method was assessed.
RESULTS Diameter estimations obtained using the SLRF method were the least scattered although diameters obtained were approximately 3 pixels greater than those measured manually. The SLRF method was the most repeatable and the Gaussian method less so. The Sobel method was the least consistent owing to frequent misinterpretation of the light reflex as the vessel edge.
CONCLUSION 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. Application of automated methods of retinal vascular analysis may be useful in the assessment of cardiovascular and other diseases.
