| Summary: | Optical Coherence Tomography Angiography (OCTA) is a non-invasive imaging
technique that has gained prominence in the diagnosis and management of various ocular
diseases. Accurate vessel segmentation in OCTA images is crucial for the quantitative
analysis of vascular structures. This study proposes a novel approach for OCTA vessel
segmentation using Hessian-based filtering and local mean enhancement. The method
involves pre-processing the images, extracting the Foveal Avascular Zone (FAZ) region,
and applying a Hessian filter to enhance blood vessels. Local mean computation and
thresholding techniques are employed for binary vessel segmentation and further
enhancement. The proposed method was evaluated on a dataset of ten OCTA images,
demonstrating promising results in vessel segmentation with improved accuracy and
efficiency compared to existing techniques. This approach has potential applications in
clinical practice and research, contributing to the better understanding and management of
ocular diseases.
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