Objective: Recognizing retinal vessel abnormity is vital to early diagnosis of ophthalmological diseases and cardiovascular events. However, segmentation results are highly influenced by elusive vessels, especially in low-contrast background lesion region. In this work, we present an end-to-end synthetic neural network, containing a symmetric equilibrium generative adversarial network (SEGAN), ...

Retinal vessel segmentation algorithms are important in automatic retinal disease screening systems. Images captured from fundus camera are useful in screening various diseases affecting retina by processing the images for better diagnosis and planning of treatment. Ophthalmology is a branch of biomedical field which requires computer aided automated techniques for pathology identification in h...

Vessel segmentation in photographies of the retina is needed in a set of computer-supported medical applications related to diagnosis and surgery planning. Considering each pixel in an image as a point in a feature space, segmentation is a binary classification problem where pixels need to be assigned to one of two classes: object and background. We describe a paradigm of hysteresis-classifier ...

Retinal blood vessel segmentation and analysis is critical for the computer-aided diagnosis of different diseases such as diabetic retinopathy. This study presents an automated unsupervised method segmenting retinal vasculature based on hybrid methods. The algorithm initially applies a preprocessing step using morphological operators to enhance tree structure against non-uniform image backgroun...