Learning features on tear film lipid layer classification

Abstract

Dry eye is a prevalent disease which leads to irritation of the ocular surface, and is associated with symptoms of discomfort and dryness. The Guillon tear film classification system is one of the most common procedures to diagnose this disease. Previous research has demonstrated that this classification can be automatized by means of image processing and machine learning techniques. However, all approaches for automatic classification have been focused on dark eyes, since they are most common in humans. This paper introduces a methodology making use of feature selection methods, to learn which features are the most relevant for each type of eyes and, thus, improving the automatic classification of the tear film lipid layer independently of the color of the eyes. Experimental results showed the adequacy of the proposed methodology, achieving classification rates over 90%, while producing unbiased results and working in real-time.