Observer models utilizing compressed textures

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

Abstract

We have previously presented a method for sorting textures based on whether they obscure a signal, and thus hinder the ability of an observer to perform a signal-detection task, or whether the presence of certain textures can be easily ignored by the observer, and thus do little to impede performance. This analysis has led to a surrogate figure of merit that was demonstrated to correlate with human-observer performance as measured by the channelized Hotelling observer. In this work, we generalize our previous results to include more tasks including estimation and combined detection/estimation tasks. We demonstrate the ability of this method to determine the textures present in a set of images that are the most detrimental to the specified task. We further devise alternative surrogate figures of merit can utilize this texture-compression method as a mechanism for generating channels for observer-performance computations.

Original languageEnglish (US)
Title of host publicationMedical Imaging 2021
Subtitle of host publicationImage Perception, Observer Performance, and Technology Assessment
EditorsFrank W. Samuelson, Sian Taylor-Phillips
PublisherSPIE
ISBN (Electronic)9781510640276
DOIs
StatePublished - 2021
EventMedical Imaging 2021: Image Perception, Observer Performance, and Technology Assessment - Virtual, Online
Duration: Feb 15 2021Feb 19 2021

Publication series

NameProgress in Biomedical Optics and Imaging - Proceedings of SPIE
Volume11599
ISSN (Print)1605-7422

Conference

ConferenceMedical Imaging 2021: Image Perception, Observer Performance, and Technology Assessment
CityVirtual, Online
Period2/15/212/19/21

Keywords

  • 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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