Estimating Mean Scatterer Spacing with the Frequency-Smoothed Spectral Autocorrelation Function

Tomy Varghese, Kevin D. Donohue

Research output: Contribution to journalArticlepeer-review

78 Scopus citations

Abstract

The quasiperiodicity of regularly spaced scatterers results in characteristic patterns in the spectra of backscattered ultrasonic signals from which the mean scatterer spacing can be estimated. The mean spacing has been considered for classifying certain biological tissue. This paper addresses the problem of estimating the mean scatterer spacing from backscattered ultrasound signals using the frequency-smoothed spectral autocorrelation (SAC) function. The SAC function exploits characteristic differences between the phase spectrum of the resolvable quasi-periodic scatterers and the unresolvable uniformly distributed (diffuse) scatterers to improve estimator performance over other estimators that operate directly on the magnitude spectrum. Mean scatterer spacing estimates are compared for the frequency-smoothed SAC function and a cepstral technique using an AR model. Simulation results indicate that SAC-based estimates converge more reliably over smaller amounts of data than cepstrum-based estimates. An example of computing an estimate from liver tissue scans is also presented for the SAC function and the AR cepstrum.

Original languageEnglish
Pages (from-to)451-463
Number of pages13
JournalIRE Transactions on Ultrasonic Engineering
Volume42
Issue number3
DOIs
StatePublished - May 1995

Bibliographical note

Funding Information:
Manuscript received July 25, 1994; revised November 15, 1994; accepted November 16, 1994. This material is based on work supported in part by the National Cancer Institute and National Institutes of Health, Grant CA52823. The authors are with the Department of Electrical Engineering University of Kentucky Lexington, KY 40503 USA. IEEE Log Number 9409994.

ASJC Scopus subject areas

  • Instrumentation
  • Acoustics and Ultrasonics
  • Electrical and Electronic Engineering

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