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

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

Research output: Contribution to journalArticlepeer-review

54 Scopus citations

Abstract

We investigate and extend the ideal observer methodology developed by Smith and Wagner to detection and discrimination tasks related to breast sonography. We provide a numerical approach for evaluating the ideal observer acting on radio frequency (RF) frame data, which involves inversion of large nonstationary covariance matrices, and we describe a power-series approach to computing this inverse. Considering a truncated power series suggests that the RF data be Wiener-filtered before forming the final envelope image. We have compared human performance for Wiener-filtered and conventional B-mode envelope images using psychophysical studies for 5 tasks related to breast cancer classification. We find significant improvements in visual detection and discrimination efficiency in four of these five tasks. We also use the Smith-Wagner approach to distinguish between human and processing inefficiencies, and find that generally the principle limitation comes from the information lost in computing the final envelope image.

Original languageEnglish (US)
Pages (from-to)198-209
Number of pages12
JournalIEEE Transactions on Medical Imaging
Volume25
Issue number2
DOIs
StatePublished - Feb 2006

Keywords

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

ASJC Scopus subject areas

  • Biomedical Engineering
  • Radiology Nuclear Medicine and imaging
  • Radiological and Ultrasound Technology
  • Electrical and Electronic Engineering
  • Computer Science Applications
  • Computational Theory and Mathematics

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