Enhanced Retinal Vessel Segmentation Using Dynamic Contrast Stretching and Mathematical Morphology on Fundus Images - El-Mehdi Chakour; Yasmine Mrad; Anass Mansouri; Yaroub Elloumi; Idriss Benatiya Andaloussi; Mohamed Hedi Bedoui; Ali Ahaitouf (Applied Computational Intelligence and Soft Computing)
Retinal vessel segmentation algorithms are crucial in automated retinal disease screening systems, as accurate determination of blood vessel structures is vital for ocular disease identification and diagnosis. In this study, we propose an efficient approach combining dynamic contrast stretching (DCS) method, advanced morphological operation–based segmentation, triangle thresholding, and a final postprocessing step. Our preprocessing step enhances the contrast of retinal images acquired under various lighting conditions, enabling reliable and accurate segmentation. This enhancement is achieved using the DCS method, which is compared to two widely used contrast enhancement techniques: adaptive histogram equalization (AHE) and contrast-limited adaptive histogram equalization (CLAHE). The second step combines morphological operations and triangle thresholding to enhance vessel structures, eliminate noise, and separate blood vessels effectively. Postprocessing addresses artifacts and ambiguous areas at image boundaries. Our approach is evaluated using widely recognized reference datasets, including Digital Retinal Images for Vessel Extraction (DRIVE), Structured Analysis of the Retina (STARE), and High-Resolution Fundus (HRF). The experimental results demonstrate that the proposed method achieves superior segmentation accuracy compared to the state-of-the-art techniques. Specifically, we achieve average accuracy rates of 98.08%, 97.14%, and 98.94% for the DRIVE, STARE, and HRF datasets, respectively. In addition, our method is distinguished by exceptionally fast execution times, reaching 0.013 s for the DRIVE and STARE datasets. These results underline the importance of our time-reduced approach to improving the accuracy and efficiency of fully automated retinal disease screening systems.