Observer-driven texture analysis in CT imaging

Matthew A. Kupinski, Zachary Garrett, Jiahua Fan

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Scopus citations


We have implemented a technique for analyzing and characterizing the textures in medical images. This technique generates a list of characteristic textures and sorts them from most important to least important for the task of detecting a specific signal in the image. The effects of the human-visual system can be incorporated into this method through the use of an eye filter. The final set of sorted textures can be quickly utilized to analyze new sets of images and make comparison regarding performance on the same task. This analysis is based upon whether the new set of images contains textures that are similar or dissimilar to that of the original set of images. We present the method for analyzing and sorting textures based on how well signals can be distinguished. We also discuss the importance of the most "obscuring" textures that make signal-detection difficult. Results and comparisons of task performance are presented.

Original languageEnglish (US)
Title of host publicationMedical Imaging 2020
Subtitle of host publicationImage Perception, Observer Performance, and Technology Assessment
EditorsFrank W. Samuelson, Sian Taylor-Phillips
ISBN (Electronic)9781510633995
StatePublished - 2020
EventMedical Imaging 2020: Image Perception, Observer Performance, and Technology Assessment - Houston, United States
Duration: Feb 19 2020Feb 20 2020

Publication series

NameProgress in Biomedical Optics and Imaging - Proceedings of SPIE
ISSN (Print)1605-7422


ConferenceMedical Imaging 2020: Image Perception, Observer Performance, and Technology Assessment
Country/TerritoryUnited States


  • CT imaging
  • Image quality
  • Model observers
  • Texture analysis

ASJC Scopus subject areas

  • Electronic, Optical and Magnetic Materials
  • Atomic and Molecular Physics, and Optics
  • Biomaterials
  • Radiology Nuclear Medicine and imaging


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