Automatic Lid Segmentation in Meibography Images

Abstract

This work presents an evaluation of two machine learning schemes for lid segmentation on meibography images. Both schemes use same input features based on pixel gray levels, laplacian and entropy filters, and also distances to anatomical landmarks like pupil and eye lashes. The methods evaluated were support vector machines (SVM) with 4th degree polynomial kernel, a Neural Network (NN) with 60 neurons distributed on three layers and the intersection of both. Performance was evaluated with AUC on a bootstrapped cross validation (CV) tests of 20 folds. Dataset is conformed by 465 images for each fold entire dataset was split on 70% training and remainder for testing. Results of CV: SVM 0.851 ± 0.103, NN 0.713 ± 0.144 and SVM & NN 0.835 ± 0.118, suggest that SVM is a suitable model to be used for this task.