Enhancing Retinal Blood Vessel Segmentation through Self-Supervised Pre-Training

Blood Vessel Segmentation in Retinal Images

Segmentation of blood vessels in retinal images allows early diagnosis of disease; automating this process provides several benefits including minimizing subjectivity and eliminating a painstaking, tedious task. Previous approaches, while satisfactory in some cases, still leave room for improvement, especially in abnormal retinal images. We propose to utilize a tracking based algorithm based on...

Blood Vessel Segmentation in Retinal Fundus Images

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...

Blood Vessel Segmentation from Retinal Images

An Effective Supervised Framework for Retinal Blood Vessel Segmentation Using Local Standardisation and Bagging

In this paper, we present a supervised framework for extracting blood vessels from retinal images. The local standardisation of the green channel of the retinal image and the Gabor filter responses at four different scales are used as features for pixel classification. The Bayesian classifier is used with a bagging framework to classify each image pixel as vessel or background. A post processin...

Supervised Blood Vessel Segmentation in Retinal Images Using Feature Based Classification

This paper presents a supervised method for blood vessel detection in digital retinal image. The use of digital images for eye disease diagnosis could be used for early detection of Diabetic Retinopathy (DR). This method of blood vessel detection uses feature based pixel classification. Membership value of these feature based image is also used to segment the blood vessel. This method uses the ...