Adaptive Higuchi's dimension-based retinal vessel diameter measurement.

Area of Science:

• Ophthalmology
• Medical Imaging
• Biomedical Engineering

Background:

• Accurate measurement of retinal vessel width is crucial for diagnosing and monitoring various eye diseases.
• Current methods often require complex image segmentation and binarization, limiting automated analysis.
• Developing robust, automated techniques for retinal vessel width measurement is an ongoing challenge.

Purpose of the Study:

• To propose and evaluate an adaptive model for measuring retinal vessel width in fundus photographs.
• To establish a method that does not rely on image segmentation or binarization.
• To assess the model's performance against existing state-of-the-art techniques.

Main Methods:

• Utilized Higuchi's fractal dimension applied to vessel cross-section profiles.
• Developed a 3D model correlating Higuchi's dimension, vessel diameter, and noise variance.
• Validated the model using the expert-annotated REVIEW public database.

Main Results:

• The adaptive model demonstrated good agreement with current state-of-the-art techniques.
• The model is tolerant to background noise in fundus images.
• Achieved subpixel precision in estimating vessel width without manual intervention.

Conclusions:

• The proposed adaptive model offers a robust and automated approach for retinal vessel width measurement.
• This method simplifies analysis by eliminating the need for segmentation and binarization.
• The model shows potential for improved diagnostic capabilities in ophthalmology.