Detail publikace

Feature Drift Resilient Tracking of the Carotid Artery Wall Using Unscented Kalman Filtering With Data Fusion

DORAZIL, J. REPP, R. KROPFREITER, T. PRÜLLER, R. ŘÍHA, K. HLAWATSCH, F.

Originální název

Feature Drift Resilient Tracking of the Carotid Artery Wall Using Unscented Kalman Filtering With Data Fusion

Typ

článek ve sborníku ve WoS nebo Scopus

Jazyk

angličtina

Originální abstrakt

An analysis of the motion of the common carotid artery (CCA) provides effective indicators for cardiovascular diseases. Here, we propose a method for tracking CCA wall motion from a B-mode ultrasound video sequence. An unscented Kalman filter based on a suitably devised state-space model fuses measurements produced by an optical flow algorithm and a CCA wall localization algorithm. This approach compensates for feature drift, which is a detrimental effect in optical flow algorithms. The proposed method is demonstrated to outperform a state-of-the-art tracking method based on optical flow.

Klíčová slova

Atherosclerosis; common carotid artery; B-mode ultrasound; unscented Kalman filter; data fusion

Autoři

DORAZIL, J.; REPP, R.; KROPFREITER, T.; PRÜLLER, R.; ŘÍHA, K.; HLAWATSCH, F.

Vydáno

4. 5. 2020

ISBN

978-1-5090-6631-5

Kniha

Proceedings of 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

ISSN

0736-7791

Periodikum

Proceedings - ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing

Stát

neuvedeno

Strany od

1095

Strany do

1099

Strany počet

5

URL

https://doi.org/10.1109/ICASSP40776.2020.9054703

BibTex

@inproceedings{BUT164233,
  author="Jan {Dorazil} and Rene {Repp} and Thomas {Kropfreiter} and Richard {Prüller} and Kamil {Říha} and Franz {Hlawatsch}",
  title="Feature Drift Resilient Tracking of the Carotid Artery Wall Using Unscented Kalman Filtering With Data Fusion",
  booktitle="Proceedings of 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)",
  year="2020",
  journal="Proceedings - ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing",
  pages="1095--1099",
  doi="10.1109/ICASSP40776.2020.9054703",
  isbn="978-1-5090-6631-5",
  issn="0736-7791",
  url="https://doi.org/10.1109/ICASSP40776.2020.9054703"
}