Automatic segmentation of blood vessels in fundus images is of great importance as eye diseases as well as some systemic diseases cause observable pathologic modifications. It is a binary classification problem: for each pixel we consider two possible classes (vessel or non-vessel). We use a GPU implementation of deep max-pooling convolutional neural networks to segment blood vessels. We test o...

Ocular fundus image assessment has been extensively used by ophthalmologists for diagnosing vascular and non vascular pathology. Examining the retinal blood vessel network may reveal arteriosclerosis, diabetes, hypertension, cardiovascular disease and stroke [12]. Furthermore, the segmentation of the vessel network is the most suitable representation for the retinal image registration since vas...

Segmentation of retinal blood vessels is significant to diagnosis and evaluation of ocular diseases like glaucoma and systemic diseases such as diabetes and hypertension. The retinal blood vessel segmentation for small and low contrast vessels is still a challenging problem. To solve this problem, a new method based on cake filter is proposed. Firstly, a quadrature filter band called cake filte...

A Bayesian method with spatial constraint is proposed for vessel segmentation in retinal images. The proposed model makes the assumption that the posterior probability of each pixel is dependent on posterior probabilities of their neighboring pixels. An energy function is defined for the proposed model. By applying the modified level set approach to minimize the proposed energy function, we can...

The segmentation and analysis of blood vessels in retinal images is of immense interest for study of diseases involving vasculature changes. An algorithm to segment blood vessels in colour retinal images by tracking of vessels is described. This algorithm proceeds by fitting a physically inspired two-dimensional model of the vessel profile to a local region of the vessel. By fitting in this man...

The segmentation of retinal blood vessels in the retina is a critical step in diagnosis of diabetic retinopathy. In this paper, we present a new method for automatically segmenting blood vessels in retinal images. Five basic algorithms for segmenting retinal blood vessels, based on different image processing techniques, are described and their strengths and weaknesses are compared. A hybrid alg...

Retinal fundus images are used in the diagnosis and treatment of many eye conditions, such as diabetic retinopathy and glaucoma. During a clinical examination, an ophthalmologist is able to determine the onset of disease by taking certain features of the retinal vessels of the fundus into account. Often the ophthalmologist will need to select what parts of a retinal fundus image constitute vess...

Artery/Vein (A/V) of retinal vessel is helpful for the automating the detection of various diseases such as Diabetic Retinography, high blood pressure, pancreas, and other vascular conditions. An automated approach is presented in this paper for classification of blood vessels into Artery/Vein based on the features extracted from the centerline pixels. The proposed method consists of 4 importan...

A software application is presented, for the detection and measurement of retinal vessels in fundoscopy images. This software is comprised by a measurement module and a Graphical User Interface (GUI) module. The measurement module employs image processing methods and operates in batch mode, to segment retinal vessels in the input image. In addition, the measurement module estimates the diameter...