CT Texture Characterization

  • Samuel G. ArmatoIIIEmail author
  • Maryellen L. Giger
  • Joseph J. Foy


Post-acquisition mathematical analysis of medical images can range from simple image processing to complex computer-aided diagnosis. The intent of such manipulation can range from the enhancement of aspects of the image for improved human visualization to artificial intelligence. The tools available for image analysis span the fields of mathematics, statistics, and computer science and incorporate biophysical aspects of the medical image acquisition system. This chapter explores the concepts and practical implementation of one specific form of mathematical manipulation of CT images: texture analysis. Texture refers to the magnitude, spatial orientation, and structure of gray-level fluctuations within an image. The quantification of image textures attempts to capture underlying relationships among the values and spatial distributions of pixels and provides a way to objectively extract quantitative information from CT scans. Texture analysis has proven to be a powerful tool over a broad array of CT applications and a wide range of radiologic tasks in CT. An important caveat for the eventual clinical use of CT texture analysis is its dependence on CT image acquisition parameters, transformations to which an image might be subjected post-acquisition, and algorithmic details of the specific texture software package employed.


Computed tomography (CT) Image texture Texture analysis Radiomics Image variability Robustness 


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Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Samuel G. ArmatoIII
    • 1
    Email author
  • Maryellen L. Giger
    • 1
  • Joseph J. Foy
    • 1
  1. 1.Department of RadiologyThe University of ChicagoChicagoUSA

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