Automatic Tortuosity Classification Using Machine Learning Approach

Abstract

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.