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  11. Scaling Of Haralick Features With Image Bit Depth And Gray Level Co-occurrence Matrix Displacement Vector For Linear Gradients

Scaling of Haralick features with image bit depth and gray level co-occurrence matrix displacement vector for linear gradients

Ana Oprisan1, Sorinel Adrian Oprisan1

  • 1Department of Physics and Astronomy, College of Charleston, Charleston, SC 29424, USA.

Computers in Biology and Medicine
|August 14, 2025

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View abstract on PubMed

Summary
This summary is machine-generated.

This study derives analytic scaling laws for Haralick texture features, enabling quantization-invariant texture classification. These new normalization methods improve reproducibility and comparability across diverse imaging datasets.

Area of Science:

  • Computer Vision
  • Image Analysis
  • Texture Recognition

Background:

  • Human perception links texture equivalence to second-order statistics, related to image gradients.
  • Current texture analysis methods struggle with variations in imaging conditions and quantization.

Purpose of the Study:

  • Derive analytic scaling laws for Haralick texture features.
  • Enable quantization-invariant and reproducible texture classification.
  • Improve texture analysis across heterogeneous imaging conditions.

Main Methods:

  • Analyzed Gray-Level Co-occurrence Matrix (GLCM) symmetries from linear image gradients.
  • Derived closed-form scaling laws for Energy, Contrast, Correlation, and Inverse Difference Moment features.
  • Developed theoretically justified normalization factors for Haralick features.
Keywords:
Displacement vectorEstimated Haralick featureGray level quantizationLinear-gradient pattern

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Main Results:

  • Demonstrated GLCM entry alignment along diagonals due to linear gradients.
  • Validated analytic scaling laws through numerical simulations.
  • Showcased superior performance of derived normalization factors over empirical methods.

Conclusions:

  • Established a principled framework for Haralick feature normalization.
  • Enhanced reproducibility, comparability, and interpretability in texture analysis.
  • Informed feature selection and classifier design for robust texture analysis in applications like radiomics and remote sensing.