9 Curvature-based Tortuosity Evaluation for Infant Retinal Images

The clinical recognition of abnormal retinal tortuosity is significant in the diagnosis of several ocular and systemic diseases. An automatic evaluation and quantification of tortuosity would help in the early detection of such pathologies. We applied two tortuosity evaluation approach based on continuous curvature to a dataset of 45 infant fundus images. Performance evaluation is done on classification accuracy of three classifiers-Naïve Bayesian classifier and k-nearest neighbor classifier, and K-means clustering algorithm, by comparing the estimated results against ground truth from expert ophthalmologists. Results show that different numerical methods provide different tortuosity values for same retinal vessels however have the potential to detect and evaluate abnormal retinal curves. The best classification accuracy of 87.3 % is achieved by the method 2 using K-nearest neighbor classifier