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Retinal Vascular Reactivity as Assessed by Optical Coherence Tomography Angiography

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[Research on Vessel Segmentation and Extraction Method Based on Improved 2D Gabor Features].

Mao Shi¹, Aihong Ji¹

¹Lab of Locomotion Bioinspiration and Intelligent Robots, College of Mechanical and Electrical Engineering, Nanjing University of Aeronautics & Astronautics, Nanjing, 210016.

Zhongguo Yi Liao Qi Xie Za Zhi = Chinese Journal of Medical Instrumentation

|April 25, 2023

Summary

This study introduces a novel vessel segmentation method using Gabor features and a U-Net neural network. The approach effectively detects small vessels and intersections in retinal images.

Keywords:

Gabor features U-Net picture processing t-SNE vessel segmentation

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Area of Science:

Medical image analysis
Computer vision
Biomedical engineering

Context:

Accurate retinal vessel segmentation is crucial for diagnosing various eye diseases.
Existing methods often struggle with small vessels and complex intersections.

Purpose:

To develop an automated vessel segmentation method utilizing Gabor features and deep learning.
To improve the detection accuracy of small and intersecting vessels.

Summary:

A novel method extracts Gabor features based on Hessian matrix eigenvectors to determine vessel direction.
A 6D feature vector is dimension-reduced to 2D and fused with the image's G channel.
A U-Net neural network classifies the fused image for precise vessel segmentation.

Impact:

The proposed method demonstrates superior performance in segmenting small vessels and challenging intersections.
This advancement has the potential to enhance early diagnosis and monitoring of retinal pathologies.
The technique offers a robust solution for automated analysis of retinal vasculature.