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Maximum Likelihood and James-Stein Edge Estimators for Left Ventricle Tracking in 3D Echocardiography

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Machine Learning in Medical Imaging (MLMI 2011)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 7009))

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Abstract

Accurate and consistent detection of endocardial borders in 3D echocardiography is a challenging task. Part of the reason for this is that the trabeculated structure of the endocardial boundary leads to alternating edge characteristics that varies over a cardiac cycle. The maximum gradient (MG), step criterion (STEP) and max flow/min cut (MFMC) edge detectors have been previously applied for the detection of endocardial edges. In this paper, we combine the responses of these edge detectors based on their confidences using maximum likelihood (MLE) and James-Stein (JS) estimators. We also present a method for utilizing the confidence-based estimates as measurements in a Kalman filter based left ventricle (LV) tracking framework. The effectiveness of the introduced methods are validated via comparative analyses among the MG, STEP, MFMC, MLE and JS.

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Author information

Authors and Affiliations

  1. Norwegian University of Science and Technology, Trondheim, Norway

    Engin Dikici

  2. GE Vingmed Ultrasound, Oslo, Norway

    Fredrik Orderud

Authors
  1. Engin Dikici
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  2. Fredrik Orderud
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Editor information

Editors and Affiliations

  1. The University of Chicago, 60637, Chicago, IL, USA

    Kenji Suzuki

  2. IBM Research Almaden, 95120, San Jose, CA, USA

    Fei Wang

  3. School of Medicine, Department of Radiology and Biomedical Research Imaging Center, University of North Carolina, 130 Mason Farm Road, 27599, Chapel Hill, NC, USA

    Dinggang Shen

  4. Xian Institute of Optics and Precision Mechanics, Chinese Academy of Sciences, 710119, Xi’an, Shaanxi, China

    Pingkun Yan

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© 2011 Springer-Verlag Berlin Heidelberg

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Dikici, E., Orderud, F. (2011). Maximum Likelihood and James-Stein Edge Estimators for Left Ventricle Tracking in 3D Echocardiography. In: Suzuki, K., Wang, F., Shen, D., Yan, P. (eds) Machine Learning in Medical Imaging. MLMI 2011. Lecture Notes in Computer Science, vol 7009. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-24319-6_6

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  • DOI: https://doi.org/10.1007/978-3-642-24319-6_6

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-24318-9

  • Online ISBN: 978-3-642-24319-6

  • eBook Packages: Computer ScienceComputer Science (R0)

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