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Three-dimensional graph-based skin layer segmentation in optical coherence tomography images for roughness

Ruchir Srivastava1, Ai Ping Yow1, Jun Cheng2

  • 1Institute for Infocomm Research, 1 Fusionopolis Way, No. 21-01 Connexis (South Tower), 138632, Singapore.

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|October 20, 2018
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Summary
This summary is machine-generated.

This study introduces a new 3D graph-based method for segmenting skin layers in optical coherence tomography (OCT) images, significantly improving accuracy in the presence of shadowing. The technique enhances skin surface analysis and roughness estimation.

Keywords:
(100.2000) Digital image processing(100.2960) Image analysis(100.6890) Three-dimensional image processing(110.4500) Optical coherence tomography(170.1870) Dermatology

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Area of Science:

  • Biomedical Imaging
  • Medical Image Analysis
  • Dermatology

Background:

  • Accurate skin layer segmentation in optical coherence tomography (OCT) is crucial for skin assessment and disease detection.
  • Existing segmentation methods struggle with shadowing artifacts caused by hair or scales in OCT images.
  • Shadowing significantly hinders reliable topographic analysis of the skin surface.

Purpose of the Study:

  • To develop an automated method for segmenting the skin surface in OCT images, specifically addressing the challenge of shadowing.
  • To improve the accuracy and robustness of skin layer segmentation compared to existing techniques.
  • To evaluate the method's utility in skin roughness estimation.

Main Methods:

  • Utilized 3D graph cuts incorporating context across multiple B-scans for segmentation.
  • Introduced a novel cost function designed to mitigate the impact of shadowing artifacts.
  • Applied the segmentation method to estimate skin roughness and compared it with manual assessments.

Main Results:

  • The proposed 3D graph cut method demonstrated a reduction in segmentation error by over 20% compared to the best previous methods.
  • The technique effectively segmented the skin surface even in the presence of significant shadowing.
  • Skin roughness estimation using the automated segmentation showed a high correlation with manual assessments.

Conclusions:

  • The novel 3D graph-based approach offers a robust solution for skin layer segmentation in OCT, particularly in challenging images with shadowing.
  • The method significantly enhances the accuracy of skin surface analysis and roughness estimation.
  • This technique shows considerable promise for clinical applications in dermatology and topographic skin assessment.