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

  • Engin Dikici
  • Fredrik Orderud
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7085)

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.

Keywords

Polynomial Regression Parametric Domain Surface Error Step Edge Endocardial Border 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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References

  1. 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. 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. 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)CrossRefGoogle Scholar
  4. 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)CrossRefGoogle Scholar
  5. 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)CrossRefGoogle Scholar
  6. 6.
    Venkatesh, S., Owens, R.A.: On the classification of image features. Pattern Recognition Letters 11, 339–349 (1990)CrossRefzbMATHGoogle Scholar
  7. 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)CrossRefGoogle Scholar
  8. 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. 9.
    Su, L.: Prediction of multivariate chaotic time series with local polynomial fitting. Computers & Mathematics with Applications 59, 737–744 (2010)MathSciNetCrossRefzbMATHGoogle Scholar
  10. 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)CrossRefGoogle Scholar
  11. 11.
    Takeda, H., Farsiu, S., Milanfar, P.: Kernel regression for image processing and reconstruction. IEEE Transactions on Image Processing 16, 349–366 (2007)MathSciNetCrossRefGoogle Scholar
  12. 12.
    Hastie, T., Tibshirani, R., Friedman, J.: The Elements of Statistical Learning: Data Mining, Inference, and Prediction, Corrected edn. Springer, Heidelberg (2003)zbMATHGoogle Scholar
  13. 13.
    Doo, D., Sabin, M.: Behaviour of recursive division surfaces near extraordinary points. Computer-Aided Design 10(6), 356–360 (1978)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Engin Dikici
    • 1
  • Fredrik Orderud
    • 2
  1. 1.Norwegian University of Science and TechnologyTrondheimNorway
  2. 2.GE Vingmed UltrasoundOsloNorway

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