Automation of flow analysis in scleral vessels based on descriptive-associative algorithms.

Area of Science:

• Ophthalmology
• Biomedical Engineering
• Medical Imaging Analysis

Background:

• Blood flow is a critical indicator of ocular health, with disruptions linked to various eye diseases.
• Assessing microcirculation in ocular vessels is challenging due to complex imaging and analysis requirements.
• Current methods for measuring blood flow in the eye have limitations regarding assumptions, equipment, and computational demands.

Purpose of the Study:

• To develop and validate a novel method for quantifying blood flow characteristics in conjunctiva vessels.
• To assess linear and volumetric blood speed, alongside topological features of the vascular network.
• To provide a tool for detecting changes in ocular blood flow relevant to disease diagnosis.

Main Methods:

• Video preprocessing to enhance image quality for analysis.
• Application of integral optical flow to determine dynamic flow characteristics.
• Analysis of conjunctiva vessel microcirculation in naturalistic eye scenes.

Main Results:

• The proposed method effectively determines key blood flow parameters, including speed and vascular topology.
• Demonstrated efficiency in analyzing blood flow within natural eye vessel imagery.
• The technique provides quantifiable metrics for assessing ocular hemodynamics.

Conclusions:

• The developed method offers a robust approach to analyzing ocular blood flow dynamics.
• It simplifies the assessment of microcirculation, aiding both novice and experienced clinicians.
• This technique can serve as a valuable diagnostic aid for various eye conditions.