ABSTRACT:The clinical recognition of abnormal retinal tortuosity enables the diagnosis of many diseases. Tortuosity is often interpreted as points of high curvature of the blood vessel along certain segments. Quantitative measures proposed so far depend on or are functions of the curvature of the vessel axis. In this paper, we propose a parallel algorithm to quantify retinal vessel tortuosity using a robust metric based on the curvature calculated from an improved chain code algorithm. We suggest that the tortuosity evaluation depends not only on the accuracy of curvature determination, but primarily on the precise determination of the region of support. The region of support, and hence the corresponding scale, was optimally selected from a quantitative experiment where it was varied from a vessel contour of two to ten pixels, before computing the curvature for each proposed metric. Scale factor optimization was based on the classification accuracy of the classifiers used, which was calculated by comparing the estimated results with ground truths from expert ophthalmologists for the integrated proposed index. We demonstrate the authenticity of the proposed metric as an indicator of changes in morphology using both simulated curves and actual vessels. The performance of each classifier is evaluated based on sensitivity, specificity, accuracy, positive predictive value, negative predictive value, and positive likelihood ratio. Our method is effective at evaluating the range of clinically relevant patterns of abnormality such as those in retinopathy of prematurity. While all the proposed metrics are sensitive to curved or kinked vessels, the integrated proposed index achieves the best sensitivity and classification rate of 97.8% and 93.6%, respectively, on 45 infant retinal images.

Retinopathy of Prematurity (ROP) is a vital cause of vision loss in premature infants, but early detection of its symptoms enables timely treatment and prevents blindness. Tortuosity is the major indicator of ROP that can potentially be automatically quantified. In this paper, which focuses on automatic tortuosity quantification and classification in images from infants at risk of ROP, we present a series of experiments on preprocessing, feature extraction, image feature selection and classification using nearest neighbor classifier. Fisher linear Discriminant analysis is used as a feature selection algorithm. We observe that the best feature set is a combination of two features: tortuosity as estimated based on combination of curvature of improved chain code and number of inflections and tortuosity as measured by inflection count metric. Accuracy, sensitivity and specificity are used as performance measures for the classifier. The results are validated against the judgments of expert ophthalmologists. The overall accuracy, sensitivity and specificity achieved on the best feature set are 95%, 95.65% and 96.74% respectively.

Abnormal dilation and tortuosity of retinal blood vessels are the primary signs of plus disease in retinopathy of prematurity. Timely prognosis could help reduce the delay in treatment and the risk of retinal detachment. Our objectives is to determine whether tortuosity and dilation sufficient for plus disease could be assessed most accurately by considering only arterioles, venules, or both. Tortuosity estimation and width measurement is done using previously proposed methods. Image preprocessing is applied before the two features namely, tortuosity and width of blood vessels are estimated to supply as input parameters for classification using K-means clustering technique. The results are validated by comparing with expert ophthalmologists ground truths. Performance is evaluated based on measures as sensitivity, specificity, predictive values and accuracy. The sensitivity, specificity, positive predictive value, negative predictive value and accuracy values obtained when considering both the arteriolar tortuosity and venous dilation are 85.86%, 90.74%, 88.76%, 88.28% and 88.50% respectively.