Feature definition, analysis and selection for cystoid region characterization in Optical Coherence Tomography

Optical Coherence Tomography (OCT) is, nowadays, a clinical standard imaging technique in opthalmology as it provides more information than other classical modalities as can be, for instance, retinographies. OCT scans show a 3D representation of the real layout of the eye fundus in a non-invasive way, letting clinicians inspect deeply the retinal layers in a cross-sectional visualization. For that reason, OCT scans are commonly used in the study of the retinal morphology and the identification of pathological structures. Among them, an appropriate identification and analysis of any present intraretinal cystoid region is crucial to perform an adequate diagnosis of the exudative macular disease, one of the main causes of blindness in developed countries. In this work, we analyzed and characterized the intraretinal cystoid regions in OCT images by the definition of a complete and heterogeneous set of 326 intensity and texture-based features. Relief-F and L0 feature selectors were used in order to identify the optimal feature subsets that provide the best discriminative power. Representative classifiers, as the Linear Bayes Normal Classifier (LDC), Quadratic Bayes Normal Classifier (QDC) and K-Nearest Neighbor Classifier (KNN) were finally used to evaluate the potential of identification of the feature subsets. The method was validated using 51 OCT images. From them, 363 and 360 samples of cystoid and non-cystoid regions were selected, respectively. The best results were offered by the LDC classifier that, using a feature subset identified by the L0 selector, provided an accuracy of 0.9060.