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Automated super-resolution image processing in ultrasound using machine learning

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

Kirsten Christensen Jeffries, Markus Schirmer, Jemma Brown, Sevan Harput, Meng Xing Tang, Christopher Dunsby, Paul Aljabar, Robert Eckersley

Original languageEnglish
Title of host publication2017 IEEE International Ultrasonics Symposium, IUS 2017
PublisherIEEE Computer Society Press
ISBN (Electronic)9781538633830
DOIs
Publication statusPublished - 31 Oct 2017
Event2017 IEEE International Ultrasonics Symposium, IUS 2017 - Washington, United States
Duration: 6 Sep 20179 Sep 2017

Conference

Conference2017 IEEE International Ultrasonics Symposium, IUS 2017
CountryUnited States
CityWashington
Period6/09/20179/09/2017

King's Authors

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.

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