Detection of Hard Exudates in Retinal Images Using a Radial Basis Function Classifier

Abstract

Diabetic retinopathy (DR) is one of the most important causes of visual impairment. Automatic recognition of DR lesions, like hard exudates (EXs), in retinal images can contribute to the diagnosis and screening of the disease. The aim of this study was to automatically detect these lesions in fundus images. To achieve this goal, each image was normalized and the candidate EX regions were segmented by a combination of global and adaptive thresholding. Then, a group of features was extracted from image regions and the subset which best discriminated between EXs and retinal background was selected by means of logistic regression (LR). This optimal subset was subsequently used as input to a radial basis function (RBF) neural network. To improve the performance of the proposed algorithm, some noisy regions were eliminated by an innovative postprocessing of the image. The main novelty of the paper is the use of LR in conjunction with RBF and the proposed postprocessing technique. Our database was composed of 117 images with variable color, brightness and quality. The database was divided into a training set of 50 images (from DR patients) and a test set of 67 images (40 from DR patients and 27 from healthy retinas). Using a lesion-based criterion (pixel resolution), a mean sensitivity of 92.1% and a mean positive predictive value of 86.4% were obtained. With an image-based criterion, a mean sensitivity of 100%, mean specificity of 70.4% and mean accuracy of 88.1% were achieved. These results suggest that the proposed method could be a diagnostic aid for ophthalmologists in the screening for DR.

Detection of Exudates on Color Fundus Images using Texture Based Feature Extraction

“…Figure 1 Visual differentiation between hard and soft retinal exudates (Kauppi et al, 2007) Various studies have examined the detection of diabetic retinopathy. García et al (2009) focused on the detection of hard exudates through lesions apparent on retinal fundus images. The candidate areas of exudates were obtained from a normalized image segmented by combining the global and adaptive thresholding methods.…”

“…That work focused on segmentation of retinal vasculature and FAZ areas using a hybrid method based on Retinex and ICA. Several researchers (Acharya et al, 2012;Amel et al, 2012;García et al, 2009;Rashid & Shagufta, 2013;S. karthick & Priyadharsini, 2014;SC et al, 2013) used CLAHE to improve the contrast of retinal fundus imagery and the uniformity of image brightness.…”

Exudates Detection with DBSCAN clustering and Back Propagation Neural Network

“…Hussain F.Jaafar et.al [4] proposed a method based on top-down image segmentation and local thresholding by a combination of edge detection and region growing Grading of hard exudates is performed. Maria Garcia.et.al [5] used a combination of global and local thresholding for segmentation of candidate exudates regions. Candidate regions are used for the training of RBF networks.…”

The Clinical Applications for Automatic Detection of Exudates

“…Some of unsupervised methods such as Principal Component Analysis (PCA) by (Li and Chutatape, 2004), k-means clustering by (Sopharak et al, 2010, Osareh et al, 2002 and Gaussian mixture models by . Example of supervised learning algorithms has also been attempted, including Neural Networks (NN) by Garcia et al, 2009)…”