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Polynomial Regression Based Edge Filtering for Left Ventricle Tracking in 3D Echocardiography

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Statistical Atlases and Computational Models of the Heart. Imaging and Modelling Challenges (STACOM 2011)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 7085))

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Abstract

Automated detection of endocardial borders in 3D echocardiography is a challenging task. Part of the reason for this is the endocardial boundary leads to alternating edge characteristics that vary 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 endocardial edge detection problem. In this paper, a local polynomial regression based method (LPR) is introduced for filtering the STEP results. For each endocardial model point, (1) the surface is parametrized locally around the point, (2) a polynomial regression is applied on the STEP edges in the parametric domain, and (3) the fitted polynomial is evaluated at the origin of the parametric domain to determine the endocardial edge position. The effectiveness of the introduced method is validated via comparative analyses among the MFMC, STEP, and first & second degree LPR methods.

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References

  1. Yang, L., Georgescu, B., Zheng, Y., Meer, P., Comaniciu, D.: 3d ultrasound tracking of the left ventricles using one-step forward prediction and data fusion of collaborative trackers. In: Proceedings of IEEE Conf. Computer Vision and Pattern Recognition (2008)

    Google Scholar 

  2. Orderud, F., Rabben, S.I.: Real-time 3d segmentation of the left ventricle using deformable subdivision surfaces. In: Proceedings of IEEE Conf. Computer Vision and Pattern Recognition (2008)

    Google Scholar 

  3. Blake, A., Isard, M.: Active Contours: The Application of Techniques from Graphics, Vision, Control Theory and Statistics to Visual Tracking of Shapes in Motion. Springer-Verlag New York, Inc., Secaucus (1998)

    Book  Google Scholar 

  4. Jacob, G., Noble, J.A., Mulet-Parada, M., Blake, A.: Evaluating a robust contour tracker on echocardiographic sequences. Medical Image Analysis 3, 63–75 (1999)

    Article  Google Scholar 

  5. Jacob, G., Noble, J.A., Kelion, A.D., Banning, A.P.: Quantitative regional analysis of myocardial wall motion. Ultrasound in Medicine & Biology 27, 773–784 (2001)

    Article  Google Scholar 

  6. Venkatesh, S., Owens, R.A.: On the classification of image features. Pattern Recognition Letters 11, 339–349 (1990)

    Article  MATH  Google Scholar 

  7. Rabben, S.I., Torp, A.H., Støylen, A., Slørdahl, S., Bjørnstad, K., Haugen, B.O., Angelsen, B.: Semiautomatic contour detection in ultrasound m-mode images. Ultrasound in Medicine & Biology 26, 287–296 (2000)

    Article  Google Scholar 

  8. Dikici, E., Orderud, F.: Graph-cut based edge detection for kalman filter based left ventricle tracking in 3d+ t echocardiography. In: Proceedings of Computing in Cardiology (2010)

    Google Scholar 

  9. Su, L.: Prediction of multivariate chaotic time series with local polynomial fitting. Computers & Mathematics with Applications 59, 737–744 (2010)

    Article  MathSciNet  MATH  Google Scholar 

  10. Palenichka, R.M., Zinterhof, P.: Structure-adaptive filtering based on polynomial regression modeling of image intensity. Journal of Electronic Imaging 10, 521–534 (2001)

    Article  Google Scholar 

  11. Takeda, H., Farsiu, S., Milanfar, P.: Kernel regression for image processing and reconstruction. IEEE Transactions on Image Processing 16, 349–366 (2007)

    Article  MathSciNet  Google Scholar 

  12. Hastie, T., Tibshirani, R., Friedman, J.: The Elements of Statistical Learning: Data Mining, Inference, and Prediction, Corrected edn. Springer, Heidelberg (2003)

    MATH  Google Scholar 

  13. Doo, D., Sabin, M.: Behaviour of recursive division surfaces near extraordinary points. Computer-Aided Design 10(6), 356–360 (1978)

    Article  Google Scholar 

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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. Department of Information and Communication Technologies, University of Pompeu Fabra, Center for ComputationalImaging and Simulation Technologies in Biomedicine (CISTIB), Plaça de la Mercè 10-12, 08002, Barcelona, Spain

    Oscar Camara

  2. Machine Learning and Perception, Microsoft Research, 7 J J Thomson Avenue, CB3 0FB, Cambridge, UK

    Ender Konukoglu

  3. Department of Medical Biophysics, Ontario Cancer Institute, princess Margaret Hospital, University of Toronto, Faculty of Medicien, 610 University Avenue, M5G 2M9, Toronto, ON, Canada

    Mihaela Pop

  4. Division of Imaging Sciences, King’s College London, St. Thomas Hospital, Lambeth Wing, SE1 7EH, London, UK

    Kawal Rhode

  5. Research Centre, Ascöepios Team, Inria Sophia Antipolis- Méditerranée, 2004 Route des Lucioles, BP 93, 06902, Sophia Antipolis Cedex, France

    Maxime Sermesant

  6. Auckland Bioengineering Institute, Medical and Health Sciences, University of Auckland, Grafton Campus, Building 502, Auckland, New Zealand

    Alistair Young

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© 2012 Springer-Verlag Berlin Heidelberg

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Dikici, E., Orderud, F. (2012). Polynomial Regression Based Edge Filtering for Left Ventricle Tracking in 3D Echocardiography. In: Camara, O., Konukoglu, E., Pop, M., Rhode, K., Sermesant, M., Young, A. (eds) Statistical Atlases and Computational Models of the Heart. Imaging and Modelling Challenges. STACOM 2011. Lecture Notes in Computer Science, vol 7085. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-28326-0_17

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  • DOI: https://doi.org/10.1007/978-3-642-28326-0_17

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-28325-3

  • Online ISBN: 978-3-642-28326-0

  • eBook Packages: Computer ScienceComputer Science (R0)

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