# Automated retinal vessel segmentation based on morphological preprocessing and 2D-Gabor wavelets

## TL;DR

This paper introduces an unsupervised retinal vessel segmentation method combining morphological preprocessing and 2D-Gabor wavelets, achieving high accuracy on publicly available datasets for improved ocular disease diagnosis.

## Contribution

The paper presents a novel unsupervised segmentation approach using morphological preprocessing and multiscale Gabor filters, outperforming existing algorithms in accuracy and sensitivity.

## Key findings

- Achieved 94.32% accuracy on DRIVE dataset
- Outperformed major algorithms in sensitivity and kappa agreement
- Effective in enhancing blood vessel pixels in complex retinal images

## Abstract

Automated segmentation of vascular map in retinal images endeavors a potential benefit in diagnostic procedure of different ocular diseases. In this paper, we suggest a new unsupervised retinal blood vessel segmentation approach using top-hat transformation, contrast-limited adaptive histogram equalization (CLAHE), and 2-D Gabor wavelet filters. Initially, retinal image is preprocessed using top-hat morphological transformation followed by CLAHE to enhance only the blood vessel pixels in the presence of exudates, optic disc, and fovea. Then, multiscale 2-D Gabor wavelet filters are applied on preprocessed image for better representation of thick and thin blood vessels located at different orientations. The efficacy of the presented algorithm is assessed on publicly available DRIVE database with manually labeled images. On DRIVE database, we achieve an average accuracy of 94.32% with a small standard deviation of 0.004. In comparison with major algorithms, our algorithm produces better performance concerning the accuracy, sensitivity, and kappa agreement.