Interferometer eye image classification for dry eye categorization using phylogenetic diversity indexes for texture analysis

•This work proposes a computational method for automatic classification of the tear film lipid layer.•This method uses the dataset of interferometric images of the tear film from the Department of Life Sciences, Glasgow Caledonian University (UK).•The proposed method addresses the problem of classification of the lipid layer interference patterns to support specialists in the diagnosis of dry eye syndrome.•The method uses phylogenetic diversity indexes for feature extraction and several classifiers.•The method achieved 97.54% of accuracy with 0.51% of standard deviation, 0.99 of ROC curve area, 0.96 of Kappa, and 0.97 of F-Measure. Background and Objective Dry eye syndrome disease negatively impacts many people in various ways. Several tests are required to diagnose it for evaluating different physiological characteristics. One of the most applied tests for this is the manual classification of tear film images captured with Doane interferometer. Interferometry images can be categorized into five groups: debris, fine fringes, coalescing fine fringes, strong fringes, and coalescing strong fringes. Instability in the tear film creates the need for an automatic system to provide experts with diagnostic support. Therefore, the purpose of this study was to propose a method for automatic classification of the tear film lipid layer using phylogenetic diversity indexes for feature extraction and several classifiers. Methods The proposed method consisted of five main steps: (1) acquisition of VOPTICAL_GCU image dataset, (2) segmentation of the region of interest, (3) feature extraction using phylogenetic diversity indexes, (4) classification using the algorithms Support Vector Machines, Random Forest, Naive Bayes, Multilayer Perceptron, Random Tree, and RBFNetwork, and, (5) validation of results. Results The best result was obtained using Random Forest classifier, reaching an accuracy of over 97%, standard deviation of 0.51%, an area under the receiver operating characteristic curve of 0.99, a Kappa index of 0.96, and an F-Measure of 0.97. Conclusions The proposed method demonstrated that the tear film lipid layer classification problem can be resolved efficiently by using phylogenetic diversity indexes.