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Medical & Biological Engineering & Computing

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10-01-2018 | Original Article

An exudate detection method for diagnosis risk of diabetic macular edema in retinal images using feature-based and supervised classification

Authors: D. Marin, M. E. Gegundez-Arias, B. Ponte, F. Alvarez, J. Garrido, C. Ortega, M. J. Vasallo, J. M. Bravo

Published in: Medical & Biological Engineering & Computing | Issue 8/2018

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Abstract

The present paper aims at presenting the methodology and first results of a detection system of risk of diabetic macular edema (DME) in fundus images. The system is based on the detection of retinal exudates (Ex), whose presence in the image is clinically used for an early diagnosis of the disease. To do so, the system applies digital image processing algorithms to the retinal image in order to obtain a set of candidate regions to be Ex, which are validated by means of feature extraction and supervised classification techniques. The diagnoses provided by the system on 1058 retinographies of 529 diabetic patients at risk of having DME show that the system can operate at a level of sensitivity comparable to that of ophthalmological specialists: it achieved 0.9000 sensitivity per patient against 0.7733, 0.9133 and 0.9000 of several specialists, where the false negatives were mild clinical cases of the disease. In addition, the level of specificity reached by the system was 0.6939, high enough to screen about 70% of the patients with no evidence of DME. These values show that the system fulfils the requirements for its possible integration into a complete diabetic retinopathy pre-screening tool for the automated management of patients within a screening programme.

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Title: An exudate detection method for diagnosis risk of diabetic macular edema in retinal images using feature-based and supervised classification
Authors: D. Marin
M. E. Gegundez-Arias
B. Ponte
F. Alvarez
J. Garrido
C. Ortega
M. J. Vasallo
J. M. Bravo
Publication date: 10-01-2018
Publisher: Springer Berlin Heidelberg
Published in: Medical & Biological Engineering & Computing / Issue 8/2018
Print ISSN: 0140-0118
Electronic ISSN: 1741-0444
DOI: https://doi.org/10.1007/s11517-017-1771-2

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