Impact of Retinal Enhancement Techniques on Blood Vessel Segmentation

The accurate segmentation of retinal blood vessels is paramount in ophthalmology for diagnosing conditions such as glaucoma, hypertension, and diabetic retinopathy. Automatic segmentation methods play a crucial role in facilitating early detection and analysis of various eye-related diseases. This paper presents an investigation into the efficacy of an adaptive contrast enhancement technique designed to improve the quality of retinal fundus images specifically for vessel segmentation. The enhancement method is integrated with a refined filtering and noise reduction algorithm, complemented by padding and color standardization techniques to enhance image quality. The proposed approach is evaluated using two widely used datasets, DRIVE and CHASE-DBl, with a comprehensive comparison of segmentation performance before and after applying the enhancement technique. Visual inspection of the results demonstrates the superiority and appropriateness of the proposed method over alternative approaches, exhibiting enhanced vessel quality that aids in the early detection and analysis of various eye-related conditions.