Motion estimation of common carotid artery wall using a H∞ filter based block matching method

Z. Gao, H. Xiong, H. Zhang, D. Wu, M. Lu, W. Wu, Kelvin Wong, Y.T. Zhang

    Research output: Chapter in Book/Conference paperConference paper

    2 Citations (Scopus)

    Abstract

    © Springer International Publishing Switzerland 2015. The movement of the common carotid artery (CCA) vessel wall has been well accepted as one important indicator of atherosclerosis, but it is still one challenge to estimate the motion of vessel wall from ultrasound images. In this paper, a robust H∞ filter was incorporated with block matching (BM) method to estimate the motion of carotid arterial wall. The performance of our method was compared with the standard BM method, Kalman filter, and manual traced method respectively on carotid artery ultrasound images from 50 subjects. Our results showed that the proposed method has a small estimation error (96 μm for the longitudinal motion and 46 μm for the radial motion), and good agreement (94.03% results fall within 95% confidence interval for the longitudinal motion and 95.53% for the radial motion) with the manual traced method. These results demonstrated the effectiveness of our method in the motion estimation of carotid wall in ultrasound images.
    Original languageEnglish
    Title of host publicationMedical Image Computing and Computer-Assisted Intervention – MICCAI 2015
    PublisherSpringer
    Pages443-450
    Volume9351
    ISBN (Print)9783319245737
    DOIs
    Publication statusPublished - 2015
    Event18th International Conference on Medical Image Computing and Computer-Assisted Intervention - Munich, Germany
    Duration: 5 Oct 20159 Oct 2015
    Conference number: 18
    https://miccai2015.org/

    Publication series

    NameLecture Notes in Computer Science (LNCS)
    PublisherSpringer
    Volume9351
    ISSN (Print)0302-9743

    Conference

    Conference18th International Conference on Medical Image Computing and Computer-Assisted Intervention
    Abbreviated titleMICCAI 2015
    CountryGermany
    CityMunich
    Period5/10/159/10/15
    Internet address

    Fingerprint

    Motion estimation
    Ultrasonics
    Kalman filters
    Error analysis

    Cite this

    Gao, Z., Xiong, H., Zhang, H., Wu, D., Lu, M., Wu, W., ... Zhang, Y. T. (2015). Motion estimation of common carotid artery wall using a H∞ filter based block matching method. In Medical Image Computing and Computer-Assisted Intervention – MICCAI 2015 (Vol. 9351, pp. 443-450). (Lecture Notes in Computer Science (LNCS); Vol. 9351). Springer. https://doi.org/10.1007/978-3-319-24574-4_53
    Gao, Z. ; Xiong, H. ; Zhang, H. ; Wu, D. ; Lu, M. ; Wu, W. ; Wong, Kelvin ; Zhang, Y.T. / Motion estimation of common carotid artery wall using a H∞ filter based block matching method. Medical Image Computing and Computer-Assisted Intervention – MICCAI 2015. Vol. 9351 Springer, 2015. pp. 443-450 (Lecture Notes in Computer Science (LNCS)).
    @inproceedings{6a36b7dc115842e19e2c06011ef71aea,
    title = "Motion estimation of common carotid artery wall using a H∞ filter based block matching method",
    abstract = "{\circledC} Springer International Publishing Switzerland 2015. The movement of the common carotid artery (CCA) vessel wall has been well accepted as one important indicator of atherosclerosis, but it is still one challenge to estimate the motion of vessel wall from ultrasound images. In this paper, a robust H∞ filter was incorporated with block matching (BM) method to estimate the motion of carotid arterial wall. The performance of our method was compared with the standard BM method, Kalman filter, and manual traced method respectively on carotid artery ultrasound images from 50 subjects. Our results showed that the proposed method has a small estimation error (96 μm for the longitudinal motion and 46 μm for the radial motion), and good agreement (94.03{\%} results fall within 95{\%} confidence interval for the longitudinal motion and 95.53{\%} for the radial motion) with the manual traced method. These results demonstrated the effectiveness of our method in the motion estimation of carotid wall in ultrasound images.",
    author = "Z. Gao and H. Xiong and H. Zhang and D. Wu and M. Lu and W. Wu and Kelvin Wong and Y.T. Zhang",
    year = "2015",
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    booktitle = "Medical Image Computing and Computer-Assisted Intervention – MICCAI 2015",
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    Gao, Z, Xiong, H, Zhang, H, Wu, D, Lu, M, Wu, W, Wong, K & Zhang, YT 2015, Motion estimation of common carotid artery wall using a H∞ filter based block matching method. in Medical Image Computing and Computer-Assisted Intervention – MICCAI 2015. vol. 9351, Lecture Notes in Computer Science (LNCS), vol. 9351, Springer, pp. 443-450, 18th International Conference on Medical Image Computing and Computer-Assisted Intervention, Munich, Germany, 5/10/15. https://doi.org/10.1007/978-3-319-24574-4_53

    Motion estimation of common carotid artery wall using a H∞ filter based block matching method. / Gao, Z.; Xiong, H.; Zhang, H.; Wu, D.; Lu, M.; Wu, W.; Wong, Kelvin; Zhang, Y.T.

    Medical Image Computing and Computer-Assisted Intervention – MICCAI 2015. Vol. 9351 Springer, 2015. p. 443-450 (Lecture Notes in Computer Science (LNCS); Vol. 9351).

    Research output: Chapter in Book/Conference paperConference paper

    TY - GEN

    T1 - Motion estimation of common carotid artery wall using a H∞ filter based block matching method

    AU - Gao, Z.

    AU - Xiong, H.

    AU - Zhang, H.

    AU - Wu, D.

    AU - Lu, M.

    AU - Wu, W.

    AU - Wong, Kelvin

    AU - Zhang, Y.T.

    PY - 2015

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    N2 - © Springer International Publishing Switzerland 2015. The movement of the common carotid artery (CCA) vessel wall has been well accepted as one important indicator of atherosclerosis, but it is still one challenge to estimate the motion of vessel wall from ultrasound images. In this paper, a robust H∞ filter was incorporated with block matching (BM) method to estimate the motion of carotid arterial wall. The performance of our method was compared with the standard BM method, Kalman filter, and manual traced method respectively on carotid artery ultrasound images from 50 subjects. Our results showed that the proposed method has a small estimation error (96 μm for the longitudinal motion and 46 μm for the radial motion), and good agreement (94.03% results fall within 95% confidence interval for the longitudinal motion and 95.53% for the radial motion) with the manual traced method. These results demonstrated the effectiveness of our method in the motion estimation of carotid wall in ultrasound images.

    AB - © Springer International Publishing Switzerland 2015. The movement of the common carotid artery (CCA) vessel wall has been well accepted as one important indicator of atherosclerosis, but it is still one challenge to estimate the motion of vessel wall from ultrasound images. In this paper, a robust H∞ filter was incorporated with block matching (BM) method to estimate the motion of carotid arterial wall. The performance of our method was compared with the standard BM method, Kalman filter, and manual traced method respectively on carotid artery ultrasound images from 50 subjects. Our results showed that the proposed method has a small estimation error (96 μm for the longitudinal motion and 46 μm for the radial motion), and good agreement (94.03% results fall within 95% confidence interval for the longitudinal motion and 95.53% for the radial motion) with the manual traced method. These results demonstrated the effectiveness of our method in the motion estimation of carotid wall in ultrasound images.

    U2 - 10.1007/978-3-319-24574-4_53

    DO - 10.1007/978-3-319-24574-4_53

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    SN - 9783319245737

    VL - 9351

    T3 - Lecture Notes in Computer Science (LNCS)

    SP - 443

    EP - 450

    BT - Medical Image Computing and Computer-Assisted Intervention – MICCAI 2015

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    Gao Z, Xiong H, Zhang H, Wu D, Lu M, Wu W et al. Motion estimation of common carotid artery wall using a H∞ filter based block matching method. In Medical Image Computing and Computer-Assisted Intervention – MICCAI 2015. Vol. 9351. Springer. 2015. p. 443-450. (Lecture Notes in Computer Science (LNCS)). https://doi.org/10.1007/978-3-319-24574-4_53