Publication detail

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.

Original Title

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

Type

conference paper

Language

English

Original Abstract

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.

Keywords

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

Authors

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

Released

4. 5. 2020

ISBN

978-1-5090-6631-5

Book

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

ISBN

0736-7791

Periodical

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

State

unknown

Pages from

1095

Pages to

1099

Pages count

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"
}