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

arXiv: https://arxiv.org/abs/1908.04123 · HTML: https://mlsanity.com/paper/1908.04123/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Aug 2019.

## Built on by (most cited first, 3 of 3)

- [A new baseline for retinal vessel segmentation: Numerical identification and correction of methodological inconsistencies affecting 100+ papers](https://mlsanity.com/paper/2111.03853/) (2021)
- [Application of Top-hat Transformation for Enhanced Blood Vessel Extraction](https://mlsanity.com/paper/2203.10005/) (2022)
- [Parametric Scaling of Preprocessing assisted U-net Architecture for Improvised Retinal Vessel Segmentation](https://mlsanity.com/paper/2203.10014/) (2022)

## Abstract (arXiv)

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.