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Segmentation of choroidal boundary in enhanced depth imaging OCTs using a multiresolution texture based modeling in

Hajar Danesh1, Raheleh Kafieh1, Hossein Rabbani1

  • 1Department of Biomedical Engineering, Medical Image and Signal Processing Research Center, Isfahan University of Medical Sciences, Isfahan 81745, Iran.

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Summary

This study introduces an automated algorithm for segmenting choroidal images from enhanced depth imaging optical coherence tomography (EDI-OCT). The novel method accurately identifies key structures like Bruch

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Area of Science:

  • Ophthalmology
  • Medical Imaging
  • Biomedical Engineering

Background:

  • Enhanced depth imaging optical coherence tomography (EDI-OCT) enables in vivo imaging of the choroid.
  • Accurate segmentation of choroidal structures is crucial for diagnosing and monitoring eye diseases.

Purpose of the Study:

  • To develop a fully automatic texture-based algorithm for segmenting choroidal images obtained from EDI-OCT.
  • To accurately segment the retinal pigment epithelium (RPE), Bruch's membrane (BM), and the choroid-sclera interface (CSI).

Main Methods:

  • A texture-based algorithm utilizing dynamic programming for RPE segmentation.
  • Gradient-based pixel searching for BM segmentation.
  • Wavelet features and Gaussian mixture models (GMM) with graph cuts for choroid-sclera interface (CSI) segmentation.

Main Results:

  • The algorithm achieved an unsigned error of 2.48 ± 0.32 pixels for BM extraction.
  • Choroid detection showed an unsigned error of 9.79 ± 3.29 pixels.
  • The proposed method demonstrated significant improvement over k-means and standard graph cut techniques.

Conclusions:

  • The developed automatic segmentation algorithm provides accurate and reliable results for EDI-OCT choroidal imaging.
  • This method offers a significant advancement for quantitative analysis of the choroid in clinical settings.
  • The algorithm shows potential for improved diagnosis and management of choroidal pathologies.