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Edge detection and mathematic fitting for corneal surface with Matlab software.

Yue Di1, Mei-Yan Li2, Tong Qiao1

  • 1Department of Ophthalmology, Shanghai Children's Hospital, Shanghai Jiaotong University, Shanghai 200062, China.

International Journal of Ophthalmology
|April 11, 2017
PubMed
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This study optimized edge detection and curve fitting for corneal surface analysis using Matlab. Conic section fitting proved optimal for accurately simulating corneal geometry and eccentricity.

Area of Science:

  • Ophthalmology
  • Biomedical Engineering
  • Computer Vision

Background:

  • Accurate corneal surface modeling is crucial for diagnosing and managing eye conditions.
  • Optical coherence tomography (OCT) provides high-resolution corneal imaging.
  • Automated and semi-automated methods are needed to efficiently analyze corneal topography.

Purpose of the Study:

  • To evaluate and select optimal edge detection algorithms for identifying the corneal surface from OCT images.
  • To compare the performance of binomial, polynomial, and conic section fitting equations for corneal surface simulation.
  • To validate the accuracy of the conic section method for calculating corneal eccentricity.

Main Methods:

  • Corneal OCT images were processed using Matlab software.
Keywords:
Matlab softwarecorneal topographycurve fittingedge detectionmathematic simulationoptical coherence tomography

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  • Five edge detection methods (Canny, Log, Prewitt, Roberts, Sobel) were applied.
  • Manual identification (ginput, getpts) and three curve fitting equations (binomial, polynomial, conic section) were compared.
  • Corneal eccentricity was calculated using corneal topography and the conic section method, then compared via paired t-test.

    • Main Results:

    • All five edge detection algorithms successfully identified continuous corneal surface edges.
    • Manual identification methods showed results slightly inside the actual corneal edges.
    • The conic section method accurately calculated parameters like tilted symmetry axis and eccentricity, with no significant difference compared to corneal topography (P=0.3760).

    Conclusions:

    • Matlab software enables feasible simulation of corneal surfaces using mathematical curves.
    • Edge detection methods offer improved repeatability and efficiency for corneal analysis.
    • While polynomial and conic sections are viable for corneal curve fitting, the conic section is the optimal choice due to its superior geometrical properties and accuracy.