Automatic segmentation of retinal blood vessels can help diagnose eye diseases like diabetic retinopathy and glaucoma earlier by supporting specialists. A method is proposed using a MATLAB code to preprocess 45 fundus images through grayscale conversion, contrast enhancement, intensity adjustment, complementing, and adaptive histogram equalization. Blood vessel segmentation then applies morphological opening, binarization, and noise extraction. The method achieved 97% specificity, 69% sensitivity, and 94% accuracy on the HRF database.

1. AUTO-SEGMENTATION OF RETINAL BLOOD VESSELS USING
IMAGE PROCESSING
Abstract
Automatic segmentation of the normal features such the blood vessels may
help to develop the future of medicine. It can give an earlier diagnosis of such eye
diseases as diabetic retinopathy and glaucoma; it can support the specialists in their
decision as well. In this paper, a method of retinal blood vessel segmentation is
proposed. The database used is HRF of a total of 45 fundus images. The fundus
images go through a MATLAB code in preprocessing steps of image acquisition,
grayscale conversion and contrast enhancement, intensity adjustment, complement
and adaptive histogram equalization. Then, the blood vessel segmentation process
includes mathematical morphological opening, binarization and noise extraction.
The proposed method shows a specificity of 97%, a sensitivity of 69% and an
Accuracy of 94%. Keywords—Segmentation; Retinal Blood Vessels; Image
Processing; HRF database