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Related Experiment Video

Updated: Mar 28, 2026

A Reproducible Computerized Method for Quantitation of Capillary Density using Nailfold Capillaroscopy

A Reproducible Computerized Method for Quantitation of Capillary Density using Nailfold Capillaroscopy

Published on: October 27, 2015

Quantification of parafoveal capillary network using a semi-automated algorithm.

Zoi Kapsala¹, Aristofanis Pallikaris, Emmanouil Ganotakis

¹Ophthalmology Department, Medical School, University of Crete, P.O. Box 2208, P.C. 71003, Heraklion, Greece.

Hellenic Journal of Nuclear Medicine

|December 15, 2015

Summary

A new semi-automated method quantifies parafoveal capillary network (PCN) morphology in fluorescein angiography (FA) images. This tool aids in diagnosing and monitoring PCN diseases and diabetic retinopathy (DR).

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Area of Science:

Ophthalmology
Medical Imaging
Computational Biology

Background:

The parafoveal capillary network (PCN) is crucial for retinal health.
Assessing PCN morphology is vital for diagnosing and monitoring eye diseases, particularly diabetic retinopathy (DR).
Existing methods for PCN analysis can be time-consuming and subjective.

Purpose of the Study:

To develop and validate a novel semi-automated computerized method for quantifying PCN morphology.
To assess the utility of this method in differentiating between various stages of diabetic retinopathy.
To establish PCN density and branch point metrics for diagnostic and monitoring purposes.

Main Methods:

A semi-automated algorithm was developed using MatLab R2011a to detect the PCN and its branch points in FA images.
The algorithm creates a one-pixel-wide skeleton of the PCN after manual delineation of the foveal avascular zone.
Capillary density and branch points were calculated within a 1000μm radius, and the method was applied to images from healthy subjects and patients with different DR stages.

Main Results:

The study successfully estimated PCN density and parafoveal capillary branch points across different subject groups.
Significant differences in these metrics were identified among groups, correlating with DR severity.
The assessed metrics effectively reflected capillary abnormalities in the central 1000μm area.

Conclusions:

The developed semi-automated method offers a potential tool for the diagnosis and monitoring of PCN diseases.
This technique can help detect subclinical abnormalities and track disease progression in diabetic retinopathy.
The quantified PCN metrics provide valuable insights into retinal microvascular changes across different DR stages.