Real-time tear film classification through cost-based feature selection

Abstract

Dry eye syndrome is an important public health problem, and can be briefly defined as a symptomatic disease which affects a wide range of population and has a negative impact on their daily activities. In clinical practice, it can be diagnosed by the observation of the tear film lipid layer patterns, and their classification into one of the Guillon categories. However, the time required to extract some features from tear film images prevents the automatic systems to work in real time. In this paper we apply a framework for cost-based feature selection to reduce this high computational time, with the particularity that it takes the cost into account when deciding which features to select. Specifically, three representative filter methods are chosen for the experiments: Correlation-Based Feature Selection (CFS), minimum- Redundancy-Maximum-Relevance (mRMR) and ReliefF. Results with a Support Vector Machine as a classifier showed that the approach is sound, since it allows to reduce considerably the computational time without significantly increasing the classification error.