Tear Film Classification Using Phylogenetic Diversity Indexes as Texture Descriptor

Dry eye is a common disease that affects the tear film and the ocular surface causing a great variety of symptoms impairing the patient daily activities. The diagnosis of this disease requires a number of tests to evaluate different physiological characteristics. One of the tests captures the appearance of the tear film using the Doane interferometer, which generates images that can be categorized into five groups: strong fringes, coalescing strong fringes, line fringes, coalescing line fringes, and debris. The images can be manually classified by specialists into one of these five groups. The use of automatic systems for the diagnosis of dry eye can assist experts in the classification of these images, contributing to more accurate exams. Therefore, this work presents a method for automatic classification of images from the the lipid layer of the tear film using phylogenetic diversity indexes to extract texture features of the images. The proposed method presents promising results, reaching accuracy rates higher than 96%.