RT info:eu-repo/semantics/article
T1 Combination of global features for the automatic quality assessment of retinal images
A1 Jiménez García, Jorge
A1 Romero Oraa, Roberto
A1 García Gadañón, María
A1 López Gálvez, María Isabel
A1 Hornero Sánchez, Roberto
K1 Diabetic retinopathy
K1 Retinopatía diabética
K1 Spectral entropy
K1 Entropía espectral
AB Diabetic retinopathy (DR) is one of the most common causes of visual loss in developed countries. Computer-aided diagnosis systems aimed at detecting DR can reduce the workload of ophthalmologists in screening programs. Nevertheless, a large number of retinal images cannot be analyzed by physicians and automatic methods due to poor quality. Automatic retinal image quality assessment (RIQA) is needed before image analysis. The purpose of this study was to combine novel generic quality features to develop a RIQA method. Several features were calculated from retinal images to achieve this goal. Features derived from the spatial and spectral entropy-based quality (SSEQ) and the natural images quality evaluator (NIQE) methods were extracted. They were combined with novel sharpness and luminosity measures based on the continuous wavelet transform (CWT) and the hue saturation value (HSV) color model, respectively. A subset of non-redundant features was selected using the fast correlation-based filter (FCBF) method. Subsequently, a multilayer perceptron (MLP) neural network was used to obtain the quality of images from the selected features. Classification results achieved 91.46% accuracy, 92.04% sensitivity, and 87.92% specificity. Results suggest that the proposed RIQA method could be applied in a more general computer-aided diagnosis system aimed at detecting a variety of retinal pathologies such as DR and age-related macular degeneration.
PB MDPI
SN 1099-4300
YR 2019
FD 2019
LK https://uvadoc.uva.es/handle/10324/56014
UL https://uvadoc.uva.es/handle/10324/56014
LA eng
NO Entropy, 2019, vol. 21, n. 3, 311
NO Producción Científica
DS UVaDOC
RD 22-nov-2024