ML-based Motion Estimation in Ultrasound Images Using Heavy-tailed Noise Distributions

Research output: Chapter in Book/Report/Conference proceedingConference contributionScientificpeer-review

Abstract

The multiplicative Rayleigh noise model has been used for maximum likelihood (ML) motion estimation in ultrasound imaging (UI). In this work, we introduce new robust similarity measures that take into account the deviations from the Rayleigh statistics resulting, for example, from multiple scatterings or acquisition artefacts. Specifically, the t-distribution is used for modelling the radio-frequency (RF) signals and the Nakagami-Gamma (NG) model is used for the echo amplitudes. Experiments using in vivo images of the carotid artery show an improvement in motion estimation accuracy in comparison with the similarity measure based on the classical Rayleigh model.

Original languageEnglish
Title of host publication2019 IEEE 8th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, CAMSAP 2019 - Proceedings
PublisherIEEE
Pages366-370
Number of pages5
ISBN (Electronic)9781728155494
DOIs
Publication statusPublished - 1 Dec 2019
MoE publication typeA4 Article in a conference publication
EventIEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing - Guadeloupe, Le Gosier, Guadeloupe
Duration: 15 Dec 201918 Dec 2019
https://camsap19.ig.umons.ac.be

Workshop

WorkshopIEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing
Abbreviated titleCAMSAP
CountryGuadeloupe
CityLe Gosier
Period15/12/201918/12/2019
Internet address

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