Quantitative Characterization of Corneal Collagen Architecture Using Intensity Gradient Modeling and Gaussian PDF Fitting

TL;DR

This paper introduces a new method to study the organization of collagen in the cornea using image analysis and probability modeling.

Contribution

A novel quantitative technique for analyzing corneal collagen organization using intensity gradient modeling and Gaussian PDF fitting is proposed.

Findings

The method performs well in noisy or low-contrast conditions compared to traditional approaches like Fourier transform.

It is applicable across species and healing stages, offering scalability and robustness.

The technique is suitable for clinical diagnostics and treatment monitoring.

Abstract

Background/Objectives: The transparency and biomechanical properties of the human cornea are governed by the precise organization of collagen fibers. A novel quantitative technique to analyze corneal collagen organization, based on intensity gradient modeling and probability density function (PDF) fitting, is proposed. Methods: Derived from second-harmonic generation (SHG) images, the method calculates image gradients, derives PDFs of gradient orientations, and fits them to Gaussian models. Results: Tested across species and temporal healing stages, this approach is an advantageous alternative to traditional methods like Fourier transform and structure tensor analyses, particularly in noisy or low-contrast conditions. Conclusions: The technique offers a scalable, robust framework suitable for research, clinical diagnostics, and treatment monitoring.

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