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

Weimin Zhou, Hua Li, Mark A. Anastasio

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

4 Scopus citations

Abstract

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
Title of host publicationMedical Imaging 2019
Subtitle of host publicationImage Perception, Observer Performance, and Technology Assessment
EditorsRobert M. Nishikawa, Frank W. Samuelson
PublisherSPIE
ISBN (Electronic)9781510625518
DOIs
StatePublished - Jan 1 2019
EventMedical Imaging 2019: Image Perception, Observer Performance, and Technology Assessment - San Diego, United States
Duration: Feb 20 2019Feb 21 2019

Publication series

NameProgress in Biomedical Optics and Imaging - Proceedings of SPIE
Volume10952
ISSN (Print)1605-7422

Conference

ConferenceMedical Imaging 2019: Image Perception, Observer Performance, and Technology Assessment
CountryUnited States
CitySan Diego
Period02/20/1902/21/19

Keywords

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

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  • Cite this

    Zhou, W., Li, H., & Anastasio, M. A. (2019). Learning the Hotelling observer for SKE detection tasks by use of supervised learning methods. In R. M. Nishikawa, & F. W. Samuelson (Eds.), Medical Imaging 2019: Image Perception, Observer Performance, and Technology Assessment [1095208] (Progress in Biomedical Optics and Imaging - Proceedings of SPIE; Vol. 10952). SPIE. https://doi.org/10.1117/12.2512607