An improved semi-supervised segmentation of the retinal vasculature using curvelet-based contrast adjustment and generalized linear model.

Diagnosis of most ophthalmic conditions, such as diabetic retinopathy, generally relies on an effective analysis of retinal blood vessels. Techniques that depend solely on the visual observation of clinicians can be tedious and prone to numerous errors. In this article, we propose a semi-supervised automated approach for segmenting blood vessels in retinal color images.

An accurate unsupervised extraction of retinal vasculature using curvelet transform and classical morphological operators.

Background: Many ophthalmic disorders such as diabetic retinopathy and hypertension can be early diagnosed by analyzing changes related to the vascular structure of the retina. Accuracy and efficiency of the segmentation of retinal blood vessels are important parameters that can help the ophthalmologist to better characterize the targeted anomalies.

Method: In this work, we propose a new method for accurate unsupervised automatic segmentation of retinal blood vessels based on a simple and adequate combination of classical filters.