Fully automated common carotid artery and internal Jugular Vein identification and tracking using B-mode ultrasound

David C. Wang, Roberta Klatzky, Bing Wu, Gregory Weller, Allan R. Sampson, George D. Stetten

Research output: Contribution to journalArticle

31 Scopus citations

Abstract

We describe a fully automated ultrasound analysis system that tracks and identifies the common carotid artery (CCA) and the internal jugular vein (IJV). Our goal is to prevent inadvertent damage to the CCA when targeting the IJV for catheterization. The automated system starts by identifying and fitting ellipses to all the regions that look like major arteries or veins throughout each B-mode ultrasound image frame. The spokes ellipse algorithm described in this paper tracks these putative vessels and calculates their characteristics, which are then weighted and summed to identify the vessels. The optimum subset of characteristics and their weights were determined from a training set of 38 subjects, whose necks were scanned with a portable 10 MHz ultrasound system at 10 frames per second. Stepwise linear discriminant analysis (LDA) narrowed the characteristics to the five that best distinguish between the CCA and IJV. A paired version of Fisher's LDA was used to calculate the weights for each of the five parameters. Leave-one-out validation studies showed that the system could track and identify the CCA and IJV with 100% accuracy in this dataset.

Original languageEnglish (US)
Article number4797854
Pages (from-to)1691-1699
Number of pages9
JournalIEEE Transactions on Biomedical Engineering
Volume56
Issue number6
DOIs
StatePublished - Jun 2009

Keywords

  • Biomedical acoustic imaging
  • Biomedical engineering
  • Biomedical image processing
  • Blood vessels

ASJC Scopus subject areas

  • Biomedical Engineering

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