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

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. Graphical Abstract Diagnosis system of risk of diabetic macular edema (DME) based on exudate (Ex) detection in fundus images.

Diabetic retinopathy detection and classification using hybrid feature set

“…This method is tested on Messidor and E‐ophtha data set. It obtains 97.3 and 96.74% ACC (Marin et al, ). Super‐elliptical convergence index filters are used for exudates detection (Dashtbozorg et al, ).…”

“…The experimental results of proposed approach are compared with nine existing approaches such as Shabbir et al (), Akram et al (), Kusakunniran et al (), Colomer, Naranjo, Janvier, and Mossi (), Issac et al (), Dashtbozorg, Zhang, Huang, and ter Haar Romeny (), Acharya et al (), and Kaur and Mittal (). Gabor wavelets and Otsu method are applied only for detection of soft‐exudates.…”

“…In the DIARETDB1 data set, Ensemble bagged tree and Bayes net outperform other classifiers. In the same (Marin et al, 2018). Super-elliptical convergence index filters are used for exudates detection (Dashtbozorg et al, 2016).…”

Diabetic Exudate Detection in Color Retinal Images

“…Another methodology used pattern recognition algorithms as D. Marin at al. [11], a feature-based and supervised classifier, was applied on 1058 fundus image corresponded to 529 patients with diabetic. Each patient had two macular centered retinal images, one of each eye.…”

Automated Exudates Detection in Retinal Fundus Image Using Morphological Operator and Entropy Maximization Thresholding

“…DR is an eye disease caused by the increase in insulin in blood and the leading cause of blindness ( [1,2]). It is a severe eye disease that affects many diabetic patients.…”