On the analysis of local and global features for hyperemia grading

In optometry, hyperemia is the accumulation of blood flow in the conjunctival tissue. Dry eye syndrome or allergic conjunctivitis are two of its main causes. Its main symptom is the presence of a red hue in the eye that optometrists evaluate according to a scale in a subjective manner. In this paper, we propose an automatic approach to the problem of hyperemia grading in the bulbar conjunctiva. We compute several image features on images of the patients' eyes, analyse the relations among them by using feature selection techniques and transform the feature vector of each image to the value in the adequate range by means of machine learning techniques. We analyse different areas of the conjunctiva to evaluate their importance for the diagnosis. Our results show that it is possible to mimic the experts' behaviour through the proposed approach.