Efficient channels for the ideal observer

Subok Park, Matthew A. Kupinski, Eric Clarkson, Harrison H. Barrett

Research output: Contribution to journalConference articlepeer-review

3 Scopus citations


For a signal-detection task, the Bayesian ideal observer is optimal among all observers because it incorporates all the statistical information of the raw data from an imaging system. The ideal observer test statistic, the likelihood ratio, is difficult to compute when uncertainties are present in backgrounds and signals. In this work, we propose a new approximation technique to estimate the likelihood ratio. This technique is a dimensionality-reduction scheme we will call the channelized-ideal observer (CIO). We can reduce the high-dimensional integrals of the ideal observer to the low-dimensional integrals of the CIO by applying a set of channels to the data. Lumpy backgrounds and circularly symmetric Gaussian signals are used for simulations studies. Laguerre-Gaussian (LG) channels have been shown to be useful for approximating ideal linear observers with these backgrounds and signals. For this reason, we choose to use LG channels for our data. The concept of efficient channels is introduced to closely approximate ideal-observer performance with the CIO for signal-known-exactly (SKE) detection tasks. Preliminary results using one to three LG channels show that the performance of the CIO is better than the channelized-Hotelling observer for the SKE detection tasks.

Original languageEnglish (US)
Article number01
Pages (from-to)12-21
Number of pages10
JournalProgress in Biomedical Optics and Imaging - Proceedings of SPIE
Issue number26
StatePublished - 2004
EventMedical Imaging 2004 - Image Perception, Observer Performance, and Technology Assessment - San Diego, CA, United States
Duration: Feb 17 2004Feb 19 2004


  • Dimensionality reduction
  • Efficient channels
  • Ideal observer
  • Image quality

ASJC Scopus subject areas

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


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