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Related Experiment Video

Updated: Jan 16, 2026

In Vivo Quantification of Hip Arthrokinematics during Dynamic Weight-bearing Activities using Dual Fluoroscopy

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Development and Evaluation of a Keypoint-Based Video Stabilization Pipeline for Oral Capillaroscopy.

Vito Gentile^1,2, Vincenzo Taormina³, Luana Conte^1,4

¹Department of Physics and Chemistry "E. Segre", University of Palermo, 90128 Palermo, Italy.

Sensors (Basel, Switzerland)

|September 27, 2025

Summary

This study presents a video stabilization model for oral capillaroscopy, significantly reducing motion noise. The developed pipeline enhances diagnostic accuracy for microcirculation analysis in the oral mucosa.

Keywords:

GFTT ORB SIFT capillaries segmentation capillaroscopy microcirculation signal enhancement video stabilization

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Area of Science:

Biomedical Engineering
Medical Imaging
Microcirculation Research

Background:

Capillaroscopy of the oral mucosa is vital for microcirculation assessment but suffers from motion artifacts.
Video instability compromises diagnostic accuracy and automated analysis of capillaries.

Purpose of the Study:

To develop and evaluate a comprehensive video stabilization model for oral capillaroscopy.
To improve the quality and reliability of videocapillaroscopy imaging for better diagnostic insights.

Main Methods:

A multi-phase pipeline integrating keypoint extraction (SIFT, ORB, GFTT), optical flow estimation, and affine transformation-based frame alignment.
Introduction of synthetic tremors using Gaussian affine transformations to simulate real-world acquisition conditions.
Performance evaluation using Structural Similarity Index Measure (SSIM) and jitter reduction metrics.

Main Results:

The proposed stabilization model effectively reduced jitter and improved image quality, achieving an average SSIM of 0.789.
All tested keypoint extraction algorithms (SIFT, ORB, GFTT) demonstrated comparable stabilization performance.
GFTT offered slightly higher structural fidelity, while ORB provided superior computational efficiency.

Conclusions:

The implemented video stabilization model significantly enhances oral capillaroscopy image quality.
The findings support the potential of this method for improving diagnostic accuracy and enabling automated capillary analysis.
The model offers comparable or superior performance to existing methods in nailfold capillaroscopy while maintaining efficiency.