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Area of Science:

  • Image analysis
  • Texture analysis
  • Computer vision

Background:

  • Haralick texture features are widely used but sensitive to Gray-Level Co-occurrence Matrix (GLCM) size.
  • Recent formulations created GLCM size-invariant features, yet these depend on the region-of-interest (ROI) sample size.

Purpose of the Study:

  • To evaluate density estimation methods for approximating GLCMs and their invariant Haralick features.
  • To assess performance across various image textures, feature types, and ROI sizes.

Main Methods:

  • Evaluated three density estimation methods: piece-wise constant distribution, Parzen-windows, and Gaussian mixture model.
  • Tested on 29 image textures and 20 invariant Haralick features with diverse ROI sizes.

Main Results:

  • Identified two feature types: those with a GLCM size error minimum and those with monotonically decreasing error.
  • Gaussian mixture model showed lowest errors for the first type, especially with small ROIs (< [Formula: see text]).

Conclusions:

  • Gaussian mixture model is recommended for invariant Haralick features with error minima, particularly for small ROIs.
  • For features with monotonically decreasing error, using a large GLCM size is the preferred approach.