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

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.

Effective Fundus Image Decomposition for the Detection of Red Lesions and Hard Exudates to Aid in the Diagnosis of Diabetic Retinopathy

“…A preprocessing stage is required to normalize the input images in order to make subsequent processing easier. In this study, we applied our method in [ 17 ], consisting of five sequential operations: bright border artifact removal, background extension, illumination and color equalization, denoising, and contrast enhancement. The retinal landmarks were notably highlighted and the intra-image and inter-image normalization was achieved.…”

“…It has pink tones in and can hinder the detection of RLs due to the high contrast it shows against the background. Separating the choroidal vasculature is useful to classify the RLs in the image, since images featuring very marked choroidal vessels tend to present false positives [ 17 ]. For this reason, the next step was to separate the layer corresponding to the choroidal vessels in .…”

“…Once the RL candidates were obtained, we used an MLP to separate the true RLs from non-RL candidates. This type of neural network has been used in previous studies for the automatic detection of RLs [ 17 , 48 ]. This stage comprises three steps:…”

“…We used the scaled conjugate gradient backpropagation method as the learning function. The cross-entropy error function was the selected error function to minimize during training [ 17 , 40 ]. In addition, we used the regularization parameter, experimentally optimized during training, to avoid overfitting and improve generalization [ 40 ].…”

“…Among the recent studies, Seoud et al [ 15 ] proposed a technique based on dynamic shape features that represent the evolution of the shape during image flooding. Other authors divided the image into superpixels to detect RLs [ 16 , 17 ]. Srivastava et al [ 18 ] proposed to apply filters on patches of different sizes and to combine the results using multiple kernel learning.…”