Fundus vessel structure segmentation based on Bel-Hat transformation

Retinal diseases such as Diabetic Retinopathy (DR), Hypertensive Retinopathy (HR), different types of Occlusions, etc., are associated with the deformity observed in the Retinal Vessel Structure (RVS). This paper proposes an automatic unsupervised vessel segmentation technique to separate the RVS with insignificant change in curvature of the vessel and eliminate the noises from the vessel structure and the background. The method involves three phases: preprocessing, where the fundus image is enhanced based on local information, and the noises are separated from the vessels. The second phase introduces a unique Bel–Hat transformation, which simultaneously uses two different groups of Structural Elements: the Neighbor Adaptive Line Structuring Element (NALSE) and the 2D Gaussian Structuring Element (2DGSE). These combined groups of Structural Elements can separate the vessel structure from the background by changing the size and orientation of the Structural Elements. Lastly, a novel robust statistical threshold is used, based on the statistical distribution of the area of the isolated objects, to segment the accurate noise-free Retinal Vessel Structure (RVS). This proposed method is more accurate than the recently proposed unsupervised and supervised methods.