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Automated super-resolution image processing in ultrasound using machine learning

Research output: Chapter in Book/Report/Conference proceedingOther chapter contribution

Standard

Automated super-resolution image processing in ultrasound using machine learning. / Jeffries, Kirsten Christensen; Schirmer, Markus; Brown, Jemma; Harput, Sevan; Tang, Meng Xing; Dunsby, Christopher; Aljabar, Paul; Eckersley, Robert.

2017 IEEE International Ultrasonics Symposium, IUS 2017. IEEE Computer Society Press, 2017. 8091563.

Research output: Chapter in Book/Report/Conference proceedingOther chapter contribution

Harvard

Jeffries, KC, Schirmer, M, Brown, J, Harput, S, Tang, MX, Dunsby, C, Aljabar, P & Eckersley, R 2017, Automated super-resolution image processing in ultrasound using machine learning. in 2017 IEEE International Ultrasonics Symposium, IUS 2017., 8091563, IEEE Computer Society Press, 2017 IEEE International Ultrasonics Symposium, IUS 2017, Washington, United States, 6/09/2017. https://doi.org/10.1109/ULTSYM.2017.8091563

APA

Jeffries, K. C., Schirmer, M., Brown, J., Harput, S., Tang, M. X., Dunsby, C., ... Eckersley, R. (2017). Automated super-resolution image processing in ultrasound using machine learning. In 2017 IEEE International Ultrasonics Symposium, IUS 2017 [8091563] IEEE Computer Society Press. https://doi.org/10.1109/ULTSYM.2017.8091563

Vancouver

Jeffries KC, Schirmer M, Brown J, Harput S, Tang MX, Dunsby C et al. Automated super-resolution image processing in ultrasound using machine learning. In 2017 IEEE International Ultrasonics Symposium, IUS 2017. IEEE Computer Society Press. 2017. 8091563 https://doi.org/10.1109/ULTSYM.2017.8091563

Author

Jeffries, Kirsten Christensen ; Schirmer, Markus ; Brown, Jemma ; Harput, Sevan ; Tang, Meng Xing ; Dunsby, Christopher ; Aljabar, Paul ; Eckersley, Robert. / Automated super-resolution image processing in ultrasound using machine learning. 2017 IEEE International Ultrasonics Symposium, IUS 2017. IEEE Computer Society Press, 2017.

Bibtex Download

@inbook{406b4c9651eb453fa979701da48d19b7,
title = "Automated super-resolution image processing in ultrasound using machine learning",
abstract = "Clinical implementation of super-resolution (SR) ultrasound imaging requires accurate single microbubble detection, and would benefit greatly from automation in order to minimize time requirements and user dependence. We present a machine learning based post-processing tool for the application of SR ultrasound imaging, where we utilize superpixelation and support vector machines (SVMs) for foreground detection and signal differentiation.",
author = "Jeffries, {Kirsten Christensen} and Markus Schirmer and Jemma Brown and Sevan Harput and Tang, {Meng Xing} and Christopher Dunsby and Paul Aljabar and Robert Eckersley",
year = "2017",
month = "10",
day = "31",
doi = "10.1109/ULTSYM.2017.8091563",
language = "English",
booktitle = "2017 IEEE International Ultrasonics Symposium, IUS 2017",
publisher = "IEEE Computer Society Press",

}

RIS (suitable for import to EndNote) Download

TY - CHAP

T1 - Automated super-resolution image processing in ultrasound using machine learning

AU - Jeffries, Kirsten Christensen

AU - Schirmer, Markus

AU - Brown, Jemma

AU - Harput, Sevan

AU - Tang, Meng Xing

AU - Dunsby, Christopher

AU - Aljabar, Paul

AU - Eckersley, Robert

PY - 2017/10/31

Y1 - 2017/10/31

N2 - Clinical implementation of super-resolution (SR) ultrasound imaging requires accurate single microbubble detection, and would benefit greatly from automation in order to minimize time requirements and user dependence. We present a machine learning based post-processing tool for the application of SR ultrasound imaging, where we utilize superpixelation and support vector machines (SVMs) for foreground detection and signal differentiation.

AB - Clinical implementation of super-resolution (SR) ultrasound imaging requires accurate single microbubble detection, and would benefit greatly from automation in order to minimize time requirements and user dependence. We present a machine learning based post-processing tool for the application of SR ultrasound imaging, where we utilize superpixelation and support vector machines (SVMs) for foreground detection and signal differentiation.

UR - http://www.scopus.com/inward/record.url?scp=85039438683&partnerID=8YFLogxK

U2 - 10.1109/ULTSYM.2017.8091563

DO - 10.1109/ULTSYM.2017.8091563

M3 - Other chapter contribution

AN - SCOPUS:85039438683

BT - 2017 IEEE International Ultrasonics Symposium, IUS 2017

PB - IEEE Computer Society Press

ER -

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