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

Nora Ouzir; Esa Ollila; Sergiy A. Vorobyov

doi:10.1109/CAMSAP45676.2019.9022664

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

Nora Ouzir, Esa Ollila, Sergiy A. Vorobyov

Tutkimustuotos: Artikkeli kirjassa/konferenssijulkaisussa › Conference contribution › Scientific › vertaisarvioitu

Abstrakti

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.

Alkuperäiskieli	Englanti
Otsikko	2019 IEEE 8th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, CAMSAP 2019 - Proceedings
Kustantaja	IEEE
Sivut	366-370
Sivumäärä	5
ISBN (elektroninen)	9781728155494
DOI - pysyväislinkit	https://doi.org/10.1109/CAMSAP45676.2019.9022664
Tila	Julkaistu - 1 jouluk. 2019
OKM-julkaisutyyppi	A4 Artikkeli konferenssijulkaisuussa
Tapahtuma	IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing - Guadeloupe, Le Gosier, Guadeloupe Kesto: 15 jouluk. 2019 → 18 jouluk. 2019 Konferenssinumero: 18 https://camsap19.ig.umons.ac.be

Workshop

Workshop	IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing
Lyhennettä	CAMSAP
Maa/Alue	Guadeloupe
Kaupunki	Le Gosier
Ajanjakso	15/12/2019 → 18/12/2019
www-osoite	https://camsap19.ig.umons.ac.be

Pääsy asiakirjaan

10.1109/CAMSAP45676.2019.9022664

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@inproceedings{6a613a4442dc4b0b926857814b8f6ba8,

title = "ML-based Motion Estimation in Ultrasound Images Using Heavy-tailed Noise Distributions",

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.",

author = "Nora Ouzir and Esa Ollila and Vorobyov, {Sergiy A.}",

year = "2019",

month = dec,

day = "1",

doi = "10.1109/CAMSAP45676.2019.9022664",

language = "English",

pages = "366--370",

booktitle = "2019 IEEE 8th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, CAMSAP 2019 - Proceedings",

publisher = "IEEE",

address = "United States",

note = "IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, CAMSAP ; Conference date: 15-12-2019 Through 18-12-2019",

url = "https://camsap19.ig.umons.ac.be",

}

Ouzir, N, Ollila, E & Vorobyov, SA 2019, ML-based Motion Estimation in Ultrasound Images Using Heavy-tailed Noise Distributions. julkaisussa 2019 IEEE 8th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, CAMSAP 2019 - Proceedings., 9022664, IEEE, Sivut 366-370, IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, Le Gosier, Guadeloupe, 15/12/2019. https://doi.org/10.1109/CAMSAP45676.2019.9022664

ML-based Motion Estimation in Ultrasound Images Using Heavy-tailed Noise Distributions. / Ouzir, Nora; Ollila, Esa ; Vorobyov, Sergiy A.

2019 IEEE 8th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, CAMSAP 2019 - Proceedings. IEEE, 2019. s. 366-370 9022664.

Tutkimustuotos: Artikkeli kirjassa/konferenssijulkaisussa › Conference contribution › Scientific › vertaisarvioitu

TY - GEN

T1 - ML-based Motion Estimation in Ultrasound Images Using Heavy-tailed Noise Distributions

AU - Ouzir, Nora

AU - Ollila, Esa

AU - Vorobyov, Sergiy A.

N1 - Conference code: 18

PY - 2019/12/1

Y1 - 2019/12/1

N2 - 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.

AB - 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.

UR - http://www.scopus.com/inward/record.url?scp=85082390844&partnerID=8YFLogxK

U2 - 10.1109/CAMSAP45676.2019.9022664

DO - 10.1109/CAMSAP45676.2019.9022664

M3 - Conference contribution

SP - 366

EP - 370

BT - 2019 IEEE 8th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, CAMSAP 2019 - Proceedings

PB - IEEE

T2 - IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing

Y2 - 15 December 2019 through 18 December 2019

ER -

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

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Workshop

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