Blood vessel detection from Retinal fundas images using GIFKCN classifier

A novel method for blood vessel detection from retinal images

BACKGROUND The morphological changes of the retinal blood vessels in retinal images are important indicators for diseases like diabetes, hypertension and glaucoma. Thus the accurate segmentation of blood vessel is of diagnostic value. METHODS In this paper, we present a novel method to segment retinal blood vessels to overcome the variations in contrast of large and thin vessels. This method ...

Blood Vessel Segmentation from Retinal Images

Vessel Junction Detection From Retinal Images

This paper presents a perceptual organization based method for detecting Vessel Junctions (VJs) from retinal images. A retinal image is first segmented into edge traces which contain vessel boundaries. Each trace is divided into generic curve segments (GCSs) at curve partitioning points (CPPs). CPPs are the places on a trace from where the continuity of GCSs was broken according to perceptual o...

Extracting Vessel Centerlines From Retinal Images Using Topographical Properties and Directional Filters

In this paper we consider the problem of blood vessel segmentation in retinal images. After enhancing the retinal image we use green channel of images for segmentation as it provides better discrimination between vessels and background. We consider the negative of retinal green channel image as a topographical surface and extract ridge points on this surface. The points with this property are l...

Automated blood vessel extraction using local features on retinal images

An automated blood vessel extraction using high-order local autocorrelation (HLAC) on retinal images is presented. Although many blood vessel extraction methods based on contrast have been proposed, a technique based on the relation of neighbor pixels has not been published. HLAC features are shift-invariant; therefore, we applied HLAC features to retinal images. However, HLAC features are weak...