ArticlePloS one2019
Gray-level invariant Haralick texture features.
Article in PloS one, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 61 papers.
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61 citing papers in PubMed.
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- Automated quantification of tumor-infiltrating lymphocytes by machine learning reveals prognostic and immunogenomic features in lung cancer.Scientific reports · 2026Article
- Article
- Scaling of Haralick features with image bit depth and gray level co-occurrence matrix displacement vector for linear gradients.Computers in biology and medicine · 2025Article
- Assessment of Influencing Factors and Robustness of Computable Image Texture Features in Digital Images.Tomography (Ann Arbor, Mich.) · 2025Article
- Visualizing Relaxation in Wearables: Multi-Domain Feature Fusion of HRV Using Fuzzy Recurrence Plots.Sensors (Basel, Switzerland) · 2025Article
- Design and Characterization of Aromatic Copolyesters Containing Furan and Isophthalic Rings with Suitable Properties for Vascular Tissue Engineering.International journal of molecular sciences · 2025Article
- Quantitative image quality metrics enable resource-efficient quality control of clinically applied AI-based reconstructions in MRI.Magma (New York, N.Y.) · 2025Article
- Feature fusion-enhanced t-SNE image atlas for geophysical features discovery.Scientific reports · 2025Article
- Synthesis of Flexible Random Copolymers of Poly(butyleneBiomacromolecules · 2025Article
- Computational analysis of whole slide images predicts PD-L1 expression and progression-free survival in immunotherapy-treated non-small cell lung cancer patients.Journal of translational medicine · 2025Article
- Matrix mechano-sensing at the invasive front induces a cytoskeletal and transcriptional memory supporting metastasis.Nature communications · 2025Article
- Contrast-enhanced magnetic resonance imaging based calf muscle perfusion and machine learning in peripheral artery disease.Scientific reports · 2025Article
- Capacity and capability of remote sensing to inform invasive plant species management in the Pacific Islands region.Conservation biology : the journal of the Society for Conservation Biology · 2025Review
- Unveiling degenerative bone changes in the condyle: a texture analysis approach using cone-beam computed tomography.Acta cirurgica brasileira · 2025Article
1 more citing papers are in PubMed but not listed here.
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5 authors.
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No grant is acknowledged in the PubMed record.
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.
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