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Super-Resolution Ultrasound Image Filtering with Machine-Learning to Reduce the Localization Error

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Sevan Harput, Enrico Grisan, Chris Dunsby, Meng Xing Tang, Long Hin Fong, Antonio Stanziola, Ge Zhang, Matthieu Toulemonde, Jiaqi Zhu, Kirsten Christensen-Jeffries, Jemma Brown, Robert J. Eckersley

Original languageEnglish
Title of host publication2019 IEEE International Ultrasonics Symposium, IUS 2019
PublisherIEEE Computer Society
Number of pages4
ISBN (Electronic)9781728145969
Publication statusPublished - 1 Oct 2019
Event2019 IEEE International Ultrasonics Symposium, IUS 2019 - Glasgow, United Kingdom
Duration: 6 Oct 20199 Oct 2019


Conference2019 IEEE International Ultrasonics Symposium, IUS 2019
CountryUnited Kingdom

King's Authors


Localization-based super-resolution imaging requires accurate detection of spatially isolated microbubbles. The reason for this requirement is that interfering or overlapping signals resulting from multiple microbubbles within the resolution limit can cause position errors. In addition to this, noise and artefacts (e.g. residual tissue signal after tissue-microbubble separation) further reduce the quality and hence the spatial resolution in SR imaging. Therefore, correctly identifying the echoes as noise, single microbubble, multiple microbubbles, or artefact is important.In this study, the use of fast classification methods for identification and rejection of non-single microbubble echoes were demonstrated. Most commonly used supervised classification methods, including Decision Trees, Discriminant Analysis, Logistic Regression, Support Vector Machine, Ensembles, k-Nearest Neighbors, and Naive Bayes, were implemented for filtering artefacts and noise in super-resolution ultrasound images. Results showed that the Ensemble method, explicitly designed to deal with unbalanced data, achieved the best result since most of the localized events are true microbubbles, which is typical for super-resolution imaging datasets.

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