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Entropy Rate Superpixel Classification for Automatic Red Lesion Detection in Fundus Images

1
Biomedical Engineering Group, E.T.S.I. de Telecomunicación, University of Valladolid, 47011 Valladolid, Spain
2
Department of Ophthalmology, Hospital Clínico Universitario de Valladolid, 47003 Valladolid, Spain
3
Instituto de Oftalmobiología Aplicada (IOBA), University of Valladolid, 47011 Valladolid, Spain
4
Instituto de Investigación en Matemáticas (IMUVA), University of Valladolid, 47011 Valladolid, Spain
5
Instituto de Neurociencias de Castilla y León (INCYL), University of Salamanca, 37007 Salamanca, Spain
*
Author to whom correspondence should be addressed.
Entropy 2019, 21(4), 417; https://doi.org/10.3390/e21040417
Received: 28 February 2019 / Revised: 17 April 2019 / Accepted: 17 April 2019 / Published: 19 April 2019
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Abstract

Diabetic retinopathy (DR) is the main cause of blindness in the working-age population in developed countries. Digital color fundus images can be analyzed to detect lesions for large-scale screening. Thereby, automated systems can be helpful in the diagnosis of this disease. The aim of this study was to develop a method to automatically detect red lesions (RLs) in retinal images, including hemorrhages and microaneurysms. These signs are the earliest indicators of DR. Firstly, we performed a novel preprocessing stage to normalize the inter-image and intra-image appearance and enhance the retinal structures. Secondly, the Entropy Rate Superpixel method was used to segment the potential RL candidates. Then, we reduced superpixel candidates by combining inaccurately fragmented regions within structures. Finally, we classified the superpixels using a multilayer perceptron neural network. The used database contained 564 fundus images. The DB was randomly divided into a training set and a test set. Results on the test set were measured using two different criteria. With a pixel-based criterion, we obtained a sensitivity of 81.43% and a positive predictive value of 86.59%. Using an image-based criterion, we reached 84.04% sensitivity, 85.00% specificity and 84.45% accuracy. The algorithm was also evaluated on the DiaretDB1 database. The proposed method could help specialists in the detection of RLs in diabetic patients. View Full-Text
Keywords: diabetic retinopathy; retinal imaging; red lesion; entropy rate superpixel segmentation; multilayer perceptron diabetic retinopathy; retinal imaging; red lesion; entropy rate superpixel segmentation; multilayer perceptron
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Romero-Oraá, R.; Jiménez-García, J.; García, M.; López-Gálvez, M.I.; Oraá-Pérez, J.; Hornero, R. Entropy Rate Superpixel Classification for Automatic Red Lesion Detection in Fundus Images. Entropy 2019, 21, 417.

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