King's College London

Research portal

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
Publication statusPublished - 31 Oct 2017
Event2017 IEEE International Ultrasonics Symposium, IUS 2017 - Washington, United States
Duration: 6 Sep 20179 Sep 2017


Conference2017 IEEE International Ultrasonics Symposium, IUS 2017
CountryUnited States

King's Authors


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.

View graph of relations

© 2018 King's College London | Strand | London WC2R 2LS | England | United Kingdom | Tel +44 (0)20 7836 5454