Medical Image Analysis
Volume 14, Issue 2 , Pages 219-226 , April 2010

Combining atlas based segmentation and intensity classification with nearest neighbor transform and accuracy weighted vote

  • Michaël Sdika

      Affiliations

    • This work is supported by CNRS (UMR 6612) and Institut Universitaire de France.
    web address

Received 1 April 2009 ,Revised 20 November 2009 ,Accepted 9 December 2009.

References 

  1. Aljabar, P., Heckemann, R., Hammers, A., Hajnal, J., Rueckert, D., 2007. Classifier selection strategies for label fusion using large atlas databases. In: Medical Image Computing and Computer-Assisted Intervention MICCAI 2007, pp. 523–531.
  2. Artaechevarria X, Munoz-Barrutia A, Ortiz-de Solorzano C. Combination strategies in multi-atlas image segmentation: application to brain MR data. IEEE Transactions on Medical Imaging. 2009;28(8):1266–1277
  3. Collins DL, Holmes CJ, Peters TM, Evans AC. Automatic 3d model-based neuroanatomical segmentation. Human Brain Mapping. 1995;3(3):190–208
  4. Collins, L.D., Zijdenbos, A.P., Baare, W.F.C., Evans, A.C., 1999. Animal+insect: Improved cortical structure segmentation. In: IPMI ’99: Proceedings of the 16th International Conference on Information Processing in Medical Imaging. Springer-Verlag, pp. 210–223.
  5. Felzenszwalb, P.F., Huttenlocher, D.P., 2004. Distance transforms of sampled functions. Technical Report, Cornell Computing and Information Science.
  6. Fischl B, Salat DH, Busa E, Albert M, Dieterich M, Haselgrove C, et al. Whole brain segmentation: automated labeling of neuroanatomical structures in the human brain. Neuron. 2002;33:341–355
  7. Fischl B, van der Kouwe A, Destrieux C, Halgren E, STgonne F, Salat DH, et al. Automatically parcellating the human cerebral cortex. Cerebral Cortex. 2004;14:11–22
  8. Heckemann RA, Hajnal JV, Aljabar P, Rueckert D, Hammers A. Automatic anatomical brain MRI segmentation combining label propagation and decision fusion. NeuroImage. 2006;33(1):115–126
  9. Isgum I, Staring M, Rutten A, Prokop M, Viergever M, van Ginneken B. Multi-atlas-based segmentation with local decision fusion – application to cardiac and aortic segmentation in CT scans. IEEE Transactions on Medical Imaging. 2009;28(7):1000–1010
  10. Maes F, Collignon A, Vandermeulen D, Marchal G, Suetens P. Multimodality image registration by maximization of mutual information. IEEE Transactions on Medical Imaging. 1997;16(2):187–198
  11. Maes, F., Leemput, K.V., DeLisi, L.E., Vandermeulen, D., Suetens, P., 1999. Quantification of cerebral grey and white matter asymmetry from MRI. In: MICCAI ’99: Proceedings of the Second International Conference on Medical Image Computing and Computer-Assisted Intervention. Springer-Verlag, London, UK, pp. 348–357.
  12. Miller, M., Christensen, G., Amit, Y., Grenander, U., 1993. Mathematical textbook of deformable neuroanatomies. In: Proceedings of the National Academy of Science of the United States of America, vol. 90, pp. 11944–11948.
  13. Otsu N. A threshold selection method from gray-level histograms. IEEE Transactions on System, Man, and Cybernetics. 1979;9(1):62–66
  14. Pohl KM, Fisher J, Grimson WEL, Kikinis R, Wells WM. A Bayesian model for joint segmentation and registration. NeuroImage. 2006;31(1):228–239
  15. Pohl KM, Wells WM, Guimond A, Kasai K, Shenton ME, Kikinis R, et al. Incorporating non-rigid registration into expectation maximization algorithm to segment MR images. In: Proceedings of the 5th International Conference on Medical Image Computing and Computer-Assisted Intervention – Part I. Springer-Verlag; 2002;p. 564–571
  16. Rohlfing T, Brandt R, Menzel R, Maurer CR. Evaluation of atlas selection strategies for atlas-based image segmentation with application to confocal microscopy images of bee brains. NeuroImage. 2004;21(4):1428–1442
  17. Rohlfing T, Maurer CR. Multi-classifier framework for atlas-based image segmentation. Pattern Recognition Letters. 2005;26(13):2070–2079
  18. Rohlfing T, Maurer CR. Shape-based averaging. IEEE Transactions on Image Processing. 2007;16(1):153–161
  19. Rohlfing T, Russakoff DB, Maurer CR. Performance-based classifier combination in atlas-based image segmentation using expectation-maximization parameter estimation. IEEE Transactions on Medical Imaging. 2004;23(8):983–994
  20. Sdika M. A fast nonrigid image registration with constraints on the Jacobian using large scale constrained optimization. IEEE Transactions on Medical Imaging. 2008;27(2):271–281
  21. Van Leemput K, Maes F, Vandermeulen D, Suetens P. Automated model-based tissue classification of MR images of the brain. IEEE Transactions on Medical Imaging. 1999;18(10):897–908
  22. Warfield S, Robatino A, Dengler J, Jolesz F, Kikinis R. Nonlinear registration and template driven segmentation. Brain Warping. 1998;9:67–84(Chapter 4)
  23. Warfield SK, Zou KH, William M, Wells I. Validation of image segmentation and expert quality with an expectation-maximization algorithm. In: Proceedings of the 5th International Conference on Medical Image Computing and Computer-Assisted Intervention – Part I. Springer-Verlag; 2002;p. 298–306
  24. Wells W, Grimson W, Kikinis R, Jolesz F. Adaptive segmentation of MRI data. IEEE Transactions on Medical Imaging. 1996;15(4):429–442
  25. Wyatt PP, Noble JA. Map MRF joint segmentation and registration of medical images. Medical Image Analysis. 2003;7(4):539–552
  26. Yeo BT, Sabuncu MR, Desikan R, Fischl B, Golland P. Effects of registration regularization and atlas sharpness on segmentation accuracy. Medical Image Analysis. 2008;12(5):603–615
  27. Zhang Y, Brady M, Smith S. Segmentation of brain MR images through a hidden Markov random field model and the expectation maximization algorithm. IEEE Transactions on Medical Imaging. 2001;20(1):45–57

PII: S1361-8415(09)00148-0

doi: 10.1016/j.media.2009.12.004

Medical Image Analysis
Volume 14, Issue 2 , Pages 219-226 , April 2010