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Diabetic retinopathy (DR) is an asymptotic complication of diabetes and the leading cause of preventable blindness in the working-age population. Early detection and treatment of DR is critical to avoid vision loss. Exudates are one of the earliest and most prevalent signs of DR. In this work, we propose a novel two-stage method for the detection and segmentation of exudates in fundus photographs. In the first stage, a fully convolutional neural network architecture is trained to segment exudates using small image patches. Next, an auxilary codebook is built from network's intermediate layer output using incremental principal component analysis. Finally, outputs of both systems are combined to produce final result. Compared to other methods, the proposed algorithm does not require computation of candidate regions or removal of other anatomical structures. Furthermore, a transfer learning approach was applied to improve the performance of the system. The proposed method was evaluated using publicly available E-Ophtha datasets. It achieved better results than the state-of-the-art methods in terms of sensitivity and specificity metrics. The proposed method accomplished better results using a diseased//not diseased evaluation scenario which indicates its applicability for screening purposes. Simplicity, performance, efficiency and robustness of the proposed method demonstrate its suitability for diabetic retinopathy screening applications.

This paper presents a novel two-stage vessel segmentation framework applied to retinal fundus images. In the first stage a convolutional neural network (CNN) is used to correlate an image patch with a corresponding groundtruth reduced using Totally Random Trees Embedding. In the second stage training patches are forward propagated through CNN to create a visual codebook. The codebook is used to build a generative nearest neighbour search space that can be queried by feature vectors created through forward propagating previously-unseen patches through CNN. The proposed framework is able to generate segmentation patches that were not seen during training. Evaluated using publicly available datasets (DRIVE, STARE) demonstrated better performance than state-of-the-art methods in terms of multiple evaluation metrics. The accuracy, robustness, speed and simplicity of the proposed framework demonstrates its suitability for automated vessel segmentation.

This paper introduces the use of fluid-dynamic modeling to determine the connectivity of overlapping venous and arterial vessels in fundus images. Analysis of the retinal vascular network may provide information related to systemic and local disorders. However, the automated identification of the vascular trees in retinal images is a challenging task due to the low signal-to-noise ratio, nonuniform illumination and the fact that fundus photography is a projection on to the imaging plane of three-dimensional retinal tissue. A zero-dimensional model was created to estimate the hemodynamic status of candidate tree configurations. Simulated annealing was used to search for an optimal configuration. Experimental results indicate that simulated annealing was very efficient on test cases that range from small to medium size networks, while ineffective on large networks. Although for large networks the nonconvexity of the cost function and the large solution space made searching for the optimal solution difficult, the accuracy (average success rate = 98.35%), and simplicity of our novel approach demonstrate its potential effectiveness in segmenting retinal vascular trees.

This paper presents a novel tool that allows a user to reconstruct the retinal vascular network from fundus images. The retinal vasculature consists of trees of arteries and veins. Common segmentation algorithms are not able to completely segment out the blood vessels in fundus images. This failure results in a set of disconnected or broken up vascular segments. Reconstructing the whole network has crucial importance because it can offer insight into global features not considered so far, including retinal fluid dynamics. This tool uses implicit neural cost functions to join vessel segments. Results have shown that the quality of the segmentation affects the outcome of connectivity algorithms and by enhancing the segmentation the connectivity can be improved.