Learning the Hotelling observer for SKE detection tasks by use of supervised learning methods

Research output: Contribution to conferencePaperpeer-review


Task-based measures of image quality (IQ) quantify the ability of an observer to perform a specific task. Such measures are commonly employed for assessing and optimizing medical imaging systems. In binary signal detection tasks, the Bayesian ideal observer (IO) sets an upper performance limit. However, the IO test statistic is generally intractable to compute when the log-likelihood ratio depends non-linearly on the measurement data. In such cases, the Hotelling observer (HO), which is the optimal linear observer, can be employed. However, traditional implementations of the HO require estimation and inversion of covariance matrices; for large images this can be computationally burdensome or even intractable. In this work, we describe a novel supervised learning- based method that employs artificial neural networks (ANNs) for estimating the HO test statistic and does not require estimation or inversion of covariance matrices. A signal-known-exactly and background-known-exactly (SKE/BKE) signal detection task is considered. The receiver operating characteristic (ROC) curve and Hotelling template corresponding to the proposed method are compared to the corresponding analytical solutions.
Original languageEnglish (US)
StatePublished - Mar 4 2019
Externally publishedYes
EventImage Perception, Observer Performance, and Technology Assessment - San Diego, United States
Duration: Feb 16 2019Feb 21 2019


ConferenceImage Perception, Observer Performance, and Technology Assessment


  • Artificial neural networks
  • Hotelling observer
  • Signal detection theory
  • Supervised learning

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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