Hybrid Learning of Vessel Segmentation in Retinal Images

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...

Vessel Segmentation in Retinal Images Using Graph-Theoretical Vessel Tracking

This paper presents a method for automatic segmentation of blood vessels in retinal images. The method is based on vessel tracking technique. The key idea of the method is that first a set of seed points (center of vessel cross sections) is extracted. Then, the seed points are connected to establish the vessel skeleton. Finally, the false vessel point are rejected by resorting to a hypothesis-v...

Blood Vessel Segmentation from Retinal Images

vessel segmentation in retinal images using multi-scale line operator and k-means clustering

detecting blood vessels is an important task in retinal image analysis.  the task is more challenging with the presence of bright and dark lesions in retinal images. here, a method is proposed to detect vessels in both normal and abnormal retinal fundus images based on their linear features. first, the negative impact of bright lesions is reduced by using k-means segmentation in a perceptive sp...