Quality assessment of retinal hyperspectral images using SURF and intensity features

Abstract

Hyperspectral (HSI) retinal imaging is an emergent modality for disease diagnosis such as diabetic retinopathy. HSI represents the retina as a 3D cube, with two spatial dimensions and one spectral, meaning that spectral signatures associated with a disease may be identified. The quality of this hypercube influences the accuracy of automatic diagnosis. Three main artifacts may limit the hypercube’s quality: parasitic contribution (e.g. blinking or ghost), uneven illumination and blurriness. We present a method for artifact detection and quality assessment using SURF features and intensity-based analysis. Quality evaluation has a rich literature in classic fundus images. However, none of these works have tackled the challenges related to HSI. Hypercubes from volunteers recruited at an eye clinic, in reflectance (48) and fluorescence (32) imaging modes, were captured using a Metabolic Hyperspectral Retinal Camera based on a tuneable light source in the visible and near infrared spectral range (450–900 nm). Compared with the ratings of two observers, our proposed method shows encouraging results in artifact detection and quality assessment.