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Gray-level invariant Haralick texture features

Author

Listed:
  • Tommy Löfstedt
  • Patrik Brynolfsson
  • Thomas Asklund
  • Tufve Nyholm
  • Anders Garpebring

Abstract

Haralick texture features are common texture descriptors in image analysis. To compute the Haralick features, the image gray-levels are reduced, a process called quantization. The resulting features depend heavily on the quantization step, so Haralick features are not reproducible unless the same quantization is performed. The aim of this work was to develop Haralick features that are invariant to the number of quantization gray-levels. By redefining the gray-level co-occurrence matrix (GLCM) as a discretized probability density function, it becomes asymptotically invariant to the quantization. The invariant and original features were compared using logistic regression classification to separate two classes based on the texture features. Classifiers trained on the invariant features showed higher accuracies, and had similar performance when training and test images had very different quantizations. In conclusion, using the invariant Haralick features, an image pattern will give the same texture feature values independent of image quantization.

Suggested Citation

  • Tommy Löfstedt & Patrik Brynolfsson & Thomas Asklund & Tufve Nyholm & Anders Garpebring, 2019. "Gray-level invariant Haralick texture features," PLOS ONE, Public Library of Science, vol. 14(2), pages 1-18, February.
  • Handle: RePEc:plo:pone00:0212110
    DOI: 10.1371/journal.pone.0212110
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    Cited by:

    1. George Amadeus Prenosil & Thilo Weitzel & Markus Fürstner & Michael Hentschel & Thomas Krause & Paul Cumming & Axel Rominger & Bernd Klaeser, 2020. "Towards guidelines to harmonize textural features in PET: Haralick textural features vary with image noise, but exposure-invariant domains enable comparable PET radiomics," PLOS ONE, Public Library of Science, vol. 15(3), pages 1-23, March.
    2. Hongdong Wang & Meng Lei & Ming Li & Yilin Chen & Jin Jiang & Liang Zou, 2019. "Intelligent Estimation of Vitrinite Reflectance of Coal from Photomicrographs Based on Machine Learning," Energies, MDPI, vol. 12(20), pages 1-16, October.

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