Title
Combination of Global Features for the Automatic Quality Assessment of Retinal Images.
Abstract
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.
Year
DOI
Venue
2019
10.3390/e21030311
ENTROPY
Keywords
Field
DocType
diabetic retinopathy,fundus images,retinal image quality assessment,Shannon entropy,spectral entropy,continuous wavelet transform,multilayer perceptron
HSL and HSV,Pattern recognition,Continuous wavelet transform,Correlation,Multilayer perceptron,Macular degeneration,Artificial intelligence,Retinal,Statistics,Artificial neural network,Entropy (information theory),Mathematics
Journal
Volume
Issue
ISSN
21
3
1099-4300
Citations 
PageRank 
References 
1
0.36
0
Authors
5