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Area of Science:

  • Medical Imaging
  • Computer Vision
  • Perceptual Science

Background:

  • Image texture, the spatial arrangement of pixel intensities, holds untapped information for scene understanding.
  • Quantitative texture analysis and human visual perception of textures are not well-integrated research areas.
  • The effect of image texture changes on human signal detection and localization in complex images is poorly understood.

Purpose of the Study:

  • To bridge the gap between quantitative texture analysis and human visual perception of textures.
  • To investigate how image texture variations impact human observer performance in signal detection and localization tasks.
  • To explore the relationship between image texture features and task-based image quality assessment.

Main Methods:

  • Utilized digital breast tomosynthesis (DBT) images, an FDA-approved tomographic X-ray method.
  • Conducted human observer studies using localization ROC (LROC) for low-contrast mass detection.
  • Employed simulated images with known ground truth to analyze changes in second-order image texture.

Main Results:

  • Changes in imaging system geometry or processing significantly alter image texture magnitudes.
  • Several established texture features extracted from digital images correlate with human observer detection-localization performance.
  • Variations in texture features provide insights into system and algorithm performance.

Conclusions:

  • Image texture features can serve as a proxy for task-based image quality assessment.
  • This approach can guide the design of imaging systems, algorithms, and filtering tools for improved perceptual benefits.
  • Caution is advised when using texture features as radiomic features due to their sensitivity to system and processing changes.