Blood vessel segmentation of retinal fundus images using dynamic preprocessing and mathematical morphology

Abstract

Accurate segmentation of blood vessels can make an important and effective contribution to the identification and diagnosis of ocular diseases such as diabetic retinopathy, glaucoma, and hypertension. Contrast enhancement is an essential component of any retinal blood vessel segmentation process. The consistency of contrast within an image will define the reliability of the segmentation. A new approach to dynamic segmentation of retinal blood vessels is proposed in this paper. Preprocessing, vessel segmentation, and post-processing are the three main stages of this method. The enhancement technique was integrated with dynamic preprocessing to improve segmentation performance. The DRIVE database was used to test the proposed method and analyze the results. The experimental results confirmed an improvement in segmentation where the use of dynamic processing increased the accuracy from 91.65% to 93.23%.