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Related Experiment Video

Updated: Jan 19, 2026

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Structured light imaging for breast-conserving surgery, part II: texture analysis and classification.

Samuel S Streeter1, Benjamin W Maloney1, David M McClatchy1

  • 1Thayer School of Engineering at Dartmouth, Optics in Medicine, Hanover, New Hampshire, United States.

Journal of Biomedical Optics
|September 16, 2019
PubMed
Summary
This summary is machine-generated.

Texture analysis of subdiffuse spatial frequency domain imaging (sd-SFDI) data aids in classifying breast tumor tissues. This machine learning approach offers efficient assessment for breast-conserving surgery tumor margins.

Keywords:
breast-conserving surgeryclassificationmachine learningspatial frequency domain imagingstructured lighttexture analysis

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Area of Science:

  • Biomedical Optics
  • Medical Imaging
  • Machine Learning in Medicine

Background:

  • Accurate assessment of tumor margins is critical in breast-conserving surgery (BCS) to minimize recurrence and optimize patient outcomes.
  • Current methods for margin assessment can be time-consuming and may not capture the full spatial heterogeneity of tumor tissues.
  • Subdiffuse spatial frequency domain imaging (sd-SFDI) offers a non-invasive optical technique for evaluating tissue properties.

Purpose of the Study:

  • To evaluate the efficacy of texture analysis of sd-SFDI data combined with a machine learning framework for classifying benign and malignant breast tumor subtypes.
  • To determine if texture features derived from sd-SFDI reflectance can differentiate between various histological categories of breast tissue resections.
  • To assess the potential of this approach as a computationally efficient tool for wide field-of-view (cm2) BCS tumor margin assessment.

Main Methods:

  • Analysis of sd-SFDI reflectance data from 42 freshly excised breast tumor resections.
  • Application of texture analysis using gray-level co-occurrence matrix (GLCM) statistics, image primitives, and power spectral density (PSD) parameters.
  • Development and application of a machine learning framework with correlation-based feature selection and fivefold cross-validation for binary classification of tissue subtypes.
  • Evaluation of classification performance using monochromatic reflectance at a specific spatial frequency (fx = 1.37 mm⁻¹) and wavelength (λ = 490 nm).

Main Results:

  • Texture metrics derived from sd-SFDI data showed statistically significant differences (p < 0.05) between specific benign and malignant breast tissue subtypes.
  • Texture-based binary classification achieved high accuracies, ranging from 0.55 (95% CI: 0.41–0.69) to 0.95 (95% CI: 0.90–1.00), depending on the tissue pair.
  • The classification performance was robust across different benign–malignant diagnosis pairs, indicating the potential of the method.

Conclusions:

  • Texture analysis of sd-SFDI data effectively differentiates between benign and malignant breast tissue subtypes.
  • This approach preserves spatial context within images and does not rely on light transport model assumptions.
  • sd-SFDI texture analysis presents a promising, computationally efficient alternative for wide field-of-view BCS tumor margin assessment compared to methods relying solely on optical scatter or color properties.