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Generation and optimization of superpixels as image processing kernels for Jones matrix optical coherence tomography.

Arata Miyazawa1, Young-Joo Hong1, Shuichi Makita1

  • 1Computational Optics Group, University of Tsukuba, Tennodai 1-1-1, Tsukuba, Ibaraki 305-8573, Japan.

Biomedical Optics Express
|October 31, 2017
PubMed
Summary

This study introduces a superpixel method for Jones matrix-based polarization sensitive optical coherence tomography (JM-OCT) image analysis. This new approach improves image sharpness and statistical accuracy for quantitative analysis of ocular tissues.

Keywords:
(100.2960) Image analysis(110.4500) Optical coherence tomography(170.4470) Ophthalmology(170.4500) Optical coherence tomography

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Area of Science:

  • Biomedical Optics
  • Medical Imaging
  • Ophthalmology

Background:

  • Jones matrix-based polarization sensitive optical coherence tomography (JM-OCT) enables simultaneous measurement of optical intensity, birefringence, degree of polarization uniformity, and OCT angiography.
  • Quantitative analysis and image processing in JM-OCT commonly rely on local statistics computed using fixed-size rectangular kernels.
  • Conventional kernels present a trade-off between image sharpness and statistical accuracy.

Purpose of the Study:

  • To introduce a novel superpixel method for JM-OCT, enabling flexible kernels for local statistics computation.
  • To develop and optimize a specialized superpixel generation algorithm for JM-OCT data.
  • To evaluate the performance of the superpixel method in preserving tissue structures for enhanced quantitative analysis.

Main Methods:

  • A superpixel method was developed, clustering JM-OCT pixels based on spatial proximity (2D cross-sectional space) and signal value proximity (four optical features).
  • This constitutes a six-dimensional clustering technique tailored for JM-OCT.
  • The algorithm and optimization methods were applied to JM-OCT datasets of posterior eyes.

Main Results:

  • The superpixel method generated flexible kernels that effectively preserved anatomical structures, including retinal layers, sclera, vessels, and retinal pigment epithelium.
  • These superpixels demonstrated superior suitability as local statistics kernels compared to traditional uniform rectangular kernels.
  • Optimization methods were detailed and validated on ocular datasets.

Conclusions:

  • The superpixel method offers a significant advancement for JM-OCT image processing and quantitative analysis.
  • It overcomes the limitations of conventional kernels, providing improved image sharpness and statistical accuracy.
  • This technique enhances the structural preservation and analytical capabilities of JM-OCT in ophthalmology.