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Abstract
Optical coherence tomography angiography (OCTA) is a label-free imaging technique widely used in ophthalmology. However, the presence of vascular axial tail artifacts makes it challenging to utilize the three-dimensional (3D) information from OCTA data. Herein, we proposed a method combining morphology-based filtering and vascular thickness characteristics to effectively suppress tail artifacts in 3D retinal OCTA images. Compared to traditional tail artifact suppression methods such as mean subtraction and Hessian filtering, our method exhibited superior performance, enabling more accurate extraction of 3D vascular architecture. Post artifact removal, the vascular structures exhibited more concentrated distributions in morpho-structural features compared to those acquired from regular OCTA data. Finally, leveraging these advantages, we achieved the differentiation between normal and diabetic retinopathy retinas through precise characterization.
Published Version
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