Retinal Blood Vessel Segmentation Approach Based on Multi-Feature Optimization Using Deep Learning Algorithm
Keywords:
Retinal Segmentation, DR, Feature Optimization, Deep Learning CNNAbstract
Retinal vessel segmentation aids ophthalmologists in diagnosing issues by revealing the vascular features in fundus images, facilitating the examination of the retina's intricate structures. Diseases such as glaucoma, age-related macular degeneration, and diabetic retinopathy are linked to morphological abnormalities in retinal blood vessels. In this study, we propose a three-stage technique for assessing the impact of retinal blood vessel segmentation. The method involves a preprocessing module, followed by double thresholds and morphological image reconstruction techniques to produce a segmented image of the vessels. The performance of the proposed method was validated using the publicly available DRIVE database, achieving sensitivity values of 0.911 and 0.921, which surpass other existing methods. Additionally, it achieved accuracy values of 0.961 and 0.954 on the STARE and DRIVE databases, respectively, comparable to other methods. This new method for retinal blood vessel segmentation can assist medical experts in diagnosing eye diseases and recommending timely treatments.
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