Observer efficiency in discrimination tasks simulating malignant and benign breast lesions with ultrasound

Craig K. Abbey, Roger J. Zemp, Jie Liu, Michael F. Insana

Research output: Contribution to journalConference article

Abstract

We investigate an ideal observer approach to signal processing in ultrasonic imaging. In two-class discrimination tasks of the sort explored in this work, the ideal observer approach rests on the use of the likelihood ratio as a test statistic. We derive this test statistic in the domain of the radio frequency (RF) signal under multivariate Gaussian assumptions, and we describe a power series approach for inverting the large covariance matrices that result. We also show how a Wiener-filter for deconvolution emerges from a first-order truncation of the power series. We then use the ideal observer approach to investigate performance in a number of tasks idealized from the use of ultrasonic imaging for the discrimination of malignant and benign breast tissue. We consider both standard B-mode processing, and the effect of Weiner filtering the RF data. We report the statistical efficiency of human observers in these tasks - as evaluated by psychophysical studies - with respect to the ideal observer. The ideal observer allows us to compute the statistical efficiency with which suboptimal observers - such as humans - perform these tasks, and how they are influenced by signal processing parameters.

Original languageEnglish (US)
Pages (from-to)183-189
Number of pages7
JournalConference Record - Asilomar Conference on Signals, Systems and Computers
Volume1
StatePublished - Dec 1 2004
Externally publishedYes
EventConference Record of the Thirty-Eighth Asilomar Conference on Signals, Systems and Computers - Pacific Grove, CA, United States
Duration: Nov 7 2004Nov 10 2004

Keywords

  • Breast cancer
  • Ideal observer
  • Image quality
  • Wiener filter

ASJC Scopus subject areas

  • Signal Processing
  • Computer Networks and Communications

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