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Pdf | this paper proposes a novel method to extract blood vessels in retinal images. The manual segmentation of the training set of labelled pixels is estimated using the quick classification of retinal vessels based on a bayesian classifier with conditional pdf (likelihoods), which describe the gaussian mixture of a probability distribution. Segmentation of retinal vessels from retinal fundus images is the key step in the automatic retinal image analysis

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In this paper we propose a new unsupervised automatic method to segment the retinal vessels from retinal fundus images. The proposed algorithm has a great potential for diagnosis application. Our work focuses on using image processing techniques in order to develop a computer program that can automatically and interactively detect and segment blood vessels in these images, thereby saving the ophthalmologist considerable time.

Retinal vessel segmentation is a critical task in fundus image analysis, providing essential insights for diagnosing various retinal diseases

In recent years, deep learning (dl) techniques, particularly generative adversarial networks (gans), have garnered significant attention for their potential to enhance medical image analysis. He automatic segmentation of retinal blood vessel is an emerging tool for medical diagnosis In this context, one of the most important problems encountered is the close contrast values between the pixels to be segmented in the image and the remaining pixels.