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Automated Tracking of the Carotid Artery in Ultrasound Image Sequences Using a Self Organizing Neural Network
KTH, School of Technology and Health (STH), Medical Engineering.ORCID iD: 0000-0002-1831-9285
2010 (English)In: Proceedings of 20th International Conference on Pattern Recognition (ICPR 2010), Istanbul, Turkey, Istanbul, Turkey, 2010, 2548-2551 p.Conference paper, Published paper (Refereed)
Abstract [en]

An automated method for the segmentation and tracking of moving vessel walls in 2D ultrasound image sequences is introduced. The method was tested on simulated and real ultrasound image sequences of the carotid artery. Tracking was achieved via a self organizing neural network known as Growing Neural Gas. This topology-preserving algorithm assigns a net of nodes connected by edges that distributes itself within the vessel walls and adapts to changes in topology with time. The movement of the nodes was analyzed to uncover the dynamics of the vessel wall. By this way, radial and longitudinal strain and strain rates have been estimated. Finally, wave intensity signals were computed from these measurements. The method proposed improves upon wave intensity wall analysis, WIWA, and opens up a possibility for easy and efficient analysis and diagnosis of vascular disease through noninvasive ultrasonic examination.

Place, publisher, year, edition, pages
Istanbul, Turkey, 2010. 2548-2551 p.
National Category
Medical Image Processing
Identifiers
URN: urn:nbn:se:kth:diva-67390DOI: 10.1109/ICPR.2010.623Scopus ID: 2-s2.0-78149492302ISBN: 978-0-7695-4109-9 (print)OAI: oai:DiVA.org:kth-67390DiVA: diva2:485222
Note

QC 20120127

Available from: 2012-01-28 Created: 2012-01-27 Last updated: 2014-02-04Bibliographically approved

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Hamid Muhammed, Hamed

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