Texture Analysis: Quantitative Insights for Radiology
*Corresponding Author: Dr. Rene Dupont, Department of Texture Analysis Imaging, University of Luxembourg, Luxembourg, Email: r.dupont@textureimg.luReceived Date: Dec 02, 2025 / Published Date: Dec 30, 2025
Citation: Dupont DR (2025) Texture Analysis: Quantitative Insights for Radiology. J Radiol 14: 758.DOI: 10.4172/2167-7964.1000758
Copyright: © 2025 Dr. Rene Dupont This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution and reproduction in any medium, provided the original author and source are credited.
Abstract
Image texture analysis is a critical quantitative method in radiology, extracting information about spatial pixel intensity arrangements to assess tissue characteristics. Its applications are broad, ranging from lesion differentiation to disease diagnosis across various imaging modalities. Deep learning has significantly advanced this field, enabling sophisticated feature extraction and improved diagnostic accuracy. Texture analysis provides objective descriptors of image heterogeneity crucial for understanding pathology and early disease detection. While radiomics and advanced analytical methods are enhancing its capabilities, challenges related to standardization and reproducibility persist. Integration with clinical and genomic data promises personalized medicine approaches. This review syntheses the current landscape of texture analysis in medical imaging, highlighting its importance, advancements, and future directions.
