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Retinal Vascular Reactivity as Assessed by Optical Coherence Tomography Angiography

Retinal Vascular Reactivity as Assessed by Optical Coherence Tomography Angiography

Published on: March 26, 2020

An automatic evaluation method for retinal image registration based on similar vessel structure matching.

Yifan Shu¹, Yunlong Feng¹, Guannan Wu¹

¹School of Information and Electronics, Beijing Institute of Technology, No. 5 South Zhong Guan Cun Street, Haidian District, Beijing, 100081, China.

Medical & Biological Engineering & Computing

|November 23, 2019

Summary

This study introduces a novel method for evaluating retinal image registration, improving accuracy for both normal and anatomically changed images. The new approach enhances clinical diagnosis by reliably screening inaccurate registrations.

Keywords:

Hungarian matching Registration evaluation Retinal image

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Area of Science:

Ophthalmology
Medical Imaging
Computer Vision

Background:

Accurate registration of retinal images is crucial for clinical diagnosis and monitoring.
Existing methods for evaluating retinal image registration perform poorly with anatomical changes.
A robust quantitative assessment is needed to compare registration algorithms and aid clinical decisions.

Purpose of the Study:

To develop an automatic and quantitative method for assessing retinal image registration performance.
To address the limitations of current evaluation methods, especially for images with anatomical variations.
To improve the reliability of retinal image registration for clinical applications.

Main Methods:

Vessel detection and skeletonization to obtain vascular centerlines.
Identification and matching of similar vessel segments using bifurcation and terminal points with the Hungarian algorithm.
Quantitative evaluation using a modified Hausdorff distance.

Main Results:

The proposed method achieves a Pearson product-moment correlation coefficient of 0.76 for normal image pairs and 0.63 for image pairs with anomalies.
Demonstrated superior performance compared to existing evaluation methods.
The method provides accurate quantitative assessment for diverse retinal image datasets.

Conclusions:

The developed method offers a reliable quantitative assessment of retinal image registration, even with anatomical changes.
This accurate evaluation facilitates the comparison of different registration techniques.
The approach can significantly aid clinical diagnosis by identifying inaccurate registrations, improving patient care.