Discriminative Learning for Anatomical Structure Detection and Segmentation

Chapter

Abstract

There is an emerging trend of using machine learning approaches to solve the tasks in medical image analysis. In this chapter, we summarize several discriminative learning methods for detection and segmentation of anatomical structures. In particular, we propose innovative detector structures, namely Probabilistic Boosting Network (PBN) and Marginal Space Learning (MSL), to address the challenges in anatomical structure detection. We also present a regression approach called Shape Regression Machine (SRM) for anatomical structure detection. For anatomical structure segmentation, we propose discriminative formulations, explicit and implicit, that are based on classification, regression and ranking.

Keywords

Manifold Covariance Pyramid Harness 

References

  1. 1.
    Adreasenm, N., Rajarethinam, R., Cizadlo, T., Arndt, S., Swayze II, V., Flashman, L., O’Leary, D., Enrhardt, J., Yuh, W.: Automatic atlas-based volume estimation of human brain regions from MR images. Journal of Computer Assisted Tomography 20(1), 98–106 (1996)CrossRefGoogle Scholar
  2. 2.
    Cootes, T., Beeston, C., Edwards, G., Taylor, C.: A unified framework for atlas matching using active appearance models. In: Proc. Information Processing in Medical Imaging (1999)Google Scholar
  3. 3.
    Cootes, T.F., Edwards, G.J., Taylor, C.J.: Active appearance models. IEEE Trans. Pattern Anal. Machine Intell. 23(6), 681–685 (2001)CrossRefGoogle Scholar
  4. 4.
    Cootes, T.F., Taylor, C.J., Cooper, D.H., Graham, J.: Active shape models—their training and application. Computer Vision and Image Understanding 61(1), 38–59 (1995)CrossRefGoogle Scholar
  5. 5.
    Covell, M.: Eigen-points: Control-point location using principal component analysis. In: International Conference on Automatic Face and Gesture Recognition, pp. 122–127. Killington, USA (1996)Google Scholar
  6. 6.
    Cristinacce, D., Cootes, T.: Boosted regression active shape models. In: Proc. British Machine Vision Conference, vol. 2, pp. 880–889 (2007)Google Scholar
  7. 7.
    Dollár, P., Tu, Z., Belongie, S.: Supervised learning of edges and object boundaries. In: Proc. IEEE Conf. Computer Vision and Pattern Recognition, pp. 1964–1971 (2006)Google Scholar
  8. 8.
    Freund, Y., Iyer, R., Schapire, R., Singer, Y.: An efficient boosting algorithm for combining preferences. J. Machine Learning Research 4(6), 933–970 (2004)MathSciNetMATHGoogle Scholar
  9. 9.
    Freund, Y., Schapire, R.E.: A decision-theoretic generalization of on-line learning and an application to boosting. J. Computer and System Sciences 55(1), 119–139 (1997)MathSciNetMATHCrossRefGoogle Scholar
  10. 10.
    Friedman, J., Hastie, T., Tibbshirani, R.: Additive logistic regression: A statistical view of boosting. The Annals of Statistics 28(2), 337–407 (2000)MathSciNetMATHCrossRefGoogle Scholar
  11. 11.
    Georgescu, B., Zhou, X.S., Comaniciu, D., Gupta, A.: Database-guided segmentation of anatomical structures with complex appearance. In: Proc. IEEE Conf. Computer Vision and Pattern Recognition (2005)Google Scholar
  12. 12.
    van Ginneken, B., Frangi, A.F., Staal, J.J., ter Haar Romeny, B.M., Viergever, M.A.: Active shape model segmentation with optimal features. IEEE Trans. Medical Imaging 21(8), 924–933 (2002)CrossRefGoogle Scholar
  13. 13.
    Hastie, T., Tibshirani, R., Friedman, J.: The Elements of Statistical Learning. Springer (2001)Google Scholar
  14. 14.
    Huang, C., Ai, H., Li, Y., Lao, S.: Vector boosting for rotation invariant multi-view face detection. In: Proc. ICCV (2005)Google Scholar
  15. 15.
    Jones, M., Viola, P.: Fast multi-view face detection. MERL-TR2003-96 (July 2003)Google Scholar
  16. 16.
    Kendall, D., Barden, D., Carne, T., Le, H.: Shape and Shape Theory. Wiley (1999)Google Scholar
  17. 17.
    Li, S., Zhang, Z.: FloatBoost learning and statistical face detection. PAMI 26, 1112–1123 (2004)CrossRefGoogle Scholar
  18. 18.
    Martin, D., Fowlkes, C., Malik, J.: Learning to detect natural image boundaries using local brightness, color and texture cues. IEEE Trans. Pattern Anal. Machine Intell. 26(5), 530–549 (2004)CrossRefGoogle Scholar
  19. 19.
    Mazziotta, J., Toga, A., Evans, A., Lancaster, J., Fox, P.: A probabilistic atlas of the human brain: Theory and rational for its development. Neuroimage 2, 89–101 (1995)CrossRefGoogle Scholar
  20. 20.
    Oren, M., Papageorgiou, C., Sinha, P., Osuna, E., Poggio, T.: Pedestrian detection using wavelet templates. In: Proc. IEEE Conf. Computer Vision and Pattern Recognition, pp. 193–199 (1997)Google Scholar
  21. 21.
    Saragih, J., Goecke, R.: A nonlinear discriminative approach to AAM fitting. In: Proc. Int’l Conf. Computer Vision. Rio de Janerio, Brazil (2007)Google Scholar
  22. 22.
    Thompson, P., Toga, A.: A framework for computational anatomy. Comput Visual Sci 5, 13–34 (2002)MATHCrossRefGoogle Scholar
  23. 23.
    Tu, Z.: Probabilistic boosting-tree: Learning discriminative methods for classification, recognition, and clustering. In: Proc. Int’l Conf. Computer Vision, pp. 1589–1596 (2005)Google Scholar
  24. 24.
    Tu, Z., Zhou, X.S., Barbu, A., Bogoni, L., Comaniciu, D.: Probabilistic 3D polyp detection in CT images: The role of sample alignment. In: Proc. IEEE Conf. Computer Vision and Pattern Recognition, pp. 1544–1551 (2006)Google Scholar
  25. 25.
    Vemuri, B., Ye, J., Chen, Y., Leonard, C.: Image registration via level-set motion: Applications to atlas-based segmentation. Medical Image Analysis 7, 1–20 (2003)CrossRefGoogle Scholar
  26. 26.
    Viola, P., Jones, M.: Rapid object detection using a boosted cascade of simple features. In: Proc. IEEE Conf. Computer Vision and Pattern Recognition, pp. 511–518 (2001)Google Scholar
  27. 27.
    Viola, P., Jones, M.: Robust real-time face detection. Int. J. Computer Vision 57(2), 137–154 (2004)CrossRefGoogle Scholar
  28. 28.
    Wu, B., AI, H., Huang, C., Lao, S.: Fast rotation invariant multi-view face detection based on real AdaBoost. In: Proc. Auto. Face and Gesture Recognition (2004)Google Scholar
  29. 29.
    Xu, C., Pham, D.L., Prince, J.L.: Medical image segmentation using deformable models. Handbook of Medical Imaging – Volume 2:Medical Image Processing and Analysis pp. 129–174 (2000)Google Scholar
  30. 30.
    Zhang, J., Zhou, S., Comaniciu, D., McMillan, L.: Conditional density learning via regression with application to deformable shape segmentation. In: Proc. CVPR (2008)Google Scholar
  31. 31.
    Zhang, J., Zhou, S., Comaniciu, D., McMillan, L.: Discriminative learning for deformable shape segmentation: A comparative study. In: Proc. ECCV (2008)Google Scholar
  32. 32.
    Zhang, J., Zhou, S., McMillan, L., Comaniciu, D.: Joint real-time object detection and pose estimation using probabilistic boosting network. In: Proc. CVPR (2007)Google Scholar
  33. 33.
    Zheng, Y., Barbu, A., Georgescu, B., Scheuering, M., Comaniciu, D.: Fast automatic heart chamber segmentation from 3D CT data using marginal space learning and steerable features. In: Proc. Int’l Conf. Computer Vision (2007)Google Scholar
  34. 34.
    Zheng, Y., Barbu, A., Georgescu, B., Scheuering, M., Comaniciu, D.: Four-chamber heart modeling and automatic segmentation for 3D cardiac CT volumes using marginal space learning and steerable features. IEEE Trans. Medical Imaging 27(11), 1668–1681 (2008)CrossRefGoogle Scholar
  35. 35.
    Zheng, Y., Georgescu, B., Ling, H., Zhou, S.K., Scheuering, M., Comaniciu, D.: Constrained marginal space learning for efficient 3D anatomical structure detection in medical images. In: Proc. IEEE Conf. Computer Vision and Pattern Recognition (2009)Google Scholar
  36. 36.
    Zheng, Y., Lu, X., Georgescu, B., Littmann, A., Mueller, E., Comaniciu, D.: Robust object detection using marginal space learning and ranking-based multi-detector aggregation: Application to automatic left ventricle detection in 2D MRI images. In: Proc. IEEE Conf. Computer Vision and Pattern Recognition (2009)Google Scholar
  37. 37.
    Zhou, S.K.: Shape regression machine and efficient segmentation of left ventricle endocardium from 2D B-mode echocardiogram. Medical Image Analysis 14(4), 563–581 (2010)CrossRefGoogle Scholar
  38. 38.
    Zhou, S.K., Park, J.H., Georgescu, B., Simopoulos, C., Otsuki, J., Comaniciu, D.: Image-based multiclass boosting and echocardiographic view classification. In: Proc. CVPR (2006)Google Scholar

Copyright information

© Springer Science+Business Media, LLC 2012

Authors and Affiliations

  1. 1.Siemens Corporation, Corporate ResearchPrincetonUSA

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