An automated method for retinal arteriovenous nicking quantification from color fundus images.

Abstract

Retinal arteriovenous (AV) nicking is one of the prominent and significant microvascular abnormalities. It is characterized by the decrease in the venular caliber at both sides of an artery-vein crossing. Recent research suggests that retinal AV nicking is a strong predictor of eye diseases such as branch retinal vein occlusion and cardiovascular diseases such as stroke. In this study, we present a novel method for objective and quantitative AV nicking assessment. From the input retinal image, the vascular network is first extracted using the multiscale line detection method. The crossover point detection method is then performed to localize all AV crossing locations. At each detected crossover point, the four vessel segments, two associated with the artery and two associated with the vein, are identified and two venular segments are then recognized through the artery-vein classification method. The vessel widths along the two venular segments are measured and analyzed to compute the AV nicking severity of that crossover. The proposed method was validated on 47 high-resolution retinal images obtained from two population-based studies. The experimental results indicate a strong correlation between the computed AV nicking values and the expert grading with a Spearman correlation coefficient of 0.70. Sensitivity was 77% and specificity was 92% (Kappa κ = 0.70) when comparing AV nicking detected using the proposed method to that detected using a manual grading method, performed by trained photographic graders.

• Authors: Uyen T V Nguyen, Alauddin Bhuiyan, Laurence A F Park, Ryo Kawasaki, Tien Y Wong, Jie Jin Wang, Paul Mitchell, Kotagiri Ramamohanarao
• Journal: IEEE transactions on bio-medical engineering (IEEE Trans Biomed Eng) Vol. 60 Issue 11 Pg. 3194-203 (Nov 2013) ISSN: 1558-2531 [Electronic] United States
• PMID: 23807422 (Publication Type: Journal Article)

Topics

• Databases, Factual
• Diagnostic Techniques, Ophthalmological
• Fundus Oculi
• Humans
• Image Processing, Computer-Assisted (methods)
• ROC Curve
• Retinal Vessels (pathology)