Vessel extraction from non-fluorescein fundus images using orientation-aware detector.

The automatic extraction of blood vessels in non-fluorescein eye fundus images is a tough task in applications such as diabetic retinopathy screening. However, vessel shapes have complex variations, and accurate modeling of retinal vascular structures is challenging. We have therefore developed a new approach to accurately extract blood vessels in non-fluorescein fundus images using an orientation-aware detector (OAD). The detector was designed according to the intrinsic property of vessels being locally oriented and having linearly elongated structures. We employ the OAD to extract vessel shapes with no assumptions on parametric orientations of vessel shapes. The orientations of vessels can be efficiently modeled by the energy distribution of Fourier transformation. Accordingly, both wide and thin vessels can be extracted with two-scale segmentation in which line operators are applied in large scale and the Gabor filter bank is applied in small scale. A post-processing technique, based on the path opening operation, is applied to eliminate false responses to nonvascular areas, such as retinal structures (optic disc and macula) and pathologies (exudates, hemorrhages,and microaneurysms). This makes the detector robust and structure-aware. By achieving a competitive CAL measurement of 80.82% for the DRIVE database and 68.94% for the STARE, the experimental results demonstrated that the OAD approach outperforms existing segmentation methods. Furthermore, the proposed approach effectively works with non-fluorescein fundus images and proves highly accurate and robust in complicated regions such as the central reflex, close vessels, and crossover points, despite a high level of illumination noise in the original data.