Retinal Blood Vessel Extraction Using a New Enhancement Technique of Modified Convolution Filters and Sauvola Thresholding

Abstract

The retinal blood vessels in humans are major components with different shapes and sizes. The extraction of the blood vessels from the retina is an important step to identify the type or nature of the pattern of the diseases in the retina. Furthermore, the retinal blood vessel was also used for diagnosis, detection, and classification. The most recent solution in this topic is to enable retinal image improvement or enhancement by a convolution filter and Sauvola threshold. In image enhancement, gamma correction is applied before filtering the retinal fundus. After that, the image should be transformed to a gray channel to enhance pictorial clarity using contrast-limited histogram equalization. For filter, this paper combines two convolution filters, namely sharpen and smooth filters. The Sauvola threshold, the morphology, and the medium filter are applied to extract blood vessels from the retinal image. This paper uses DRIVE and STARE datasets. The accuracies of the proposed method are 95.37% for DRIVE with a runtime of 1.77[Formula: see text]s and 95.17% for STARE with 2.05[Formula: see text]s runtime. Based on the result, it concludes that the proposed method is good enough to achieve average calculation parameters of a low time quality, quick, and significant.

Biometric System for Person Authentication Using Retinal Vascular Branching Pattern

“…The use of dilation-based surgery on retinal blood vessel images results in removing some of the severed blood vessel objects so that the entire retinal blood vessel is not extracted properly. This research will overcome the problems raised in previous studies [10] [11] [12] [13] by eliminating some errors in the process of extracting features of the retinal blood vessels as a feature that is truly unique to be included in the retinal biometric system by proposing an algorithm for the formation of branching combinations (bifurcation and trifurcation) and crossover through tracing of skeletal blood vessel images which in previous studies used node search operations with DFS [10] which cannot find trifurcation and crossover points. Another difficulty in extracting retinal blood vessels is that other objects in retinal fundus images are often extracted as part of blood vessels (for example : optic disc), thus can reduce the accuracy of identification in retinal biometric systems.…”

“…The central branch artery and central vein artery cannot be extracted properly [10]. Retinal feature extraction was performed [11] using a combination of a median filter and the first derivative of the Gaussian filter [12]. The median filter is used to eliminate noise in the form of information outside the retinal blood vessels (e.g.…”