Superpixel and Entropy-Based Multi-atlas Fusion Framework for the Segmentation of X-ray Images

  • Dac Cong Tai Nguyen
  • Said BenameurEmail author
  • Max Mignotte
  • Frédéric Lavoie
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9280)


X-ray images segmentation can be useful to aid in accurate diagnosis or faithful 3D bone reconstruction but remains a challenging and complex task, particularly when dealing with large and complex anatomical structures such as the human pelvic bone. In this paper, we propose a multi-atlas fusion framework to automatically segment the human pelvic structure from 45 or 135-degree oblique X-ray radiographic images. Unlike most atlas-based approach, this method combines a data set of a priori segmented X-ray images of the human pelvis (or multi-atlas) to generate an adaptive superpixel map in order to take efficiently into account both the imaging pose variability along with the inter-patient (bone) shape non-linear variability. In addition, we propose a new label propagation or fusion step based on the variation of information criterion for integrating the multi-atlas information into the final consensus segmentation. We thoroughly evaluated the method on 30 manually segmented 45 or 135 degree oblique X-ray radiographic images data set by performing a leave-one-out study. Compared to the manual gold standard segmentations, the accuracy of our automatic segmentation approach is \(85\%\) which remains in the error range of manual segmentations due to the inter intra/observer variability.


Consensus segmentation X-ray images Multi-atlas segmentation Variation of information based fusion step Superpixel map 


  1. 1.
    Mignotte, M., Collet, C., Pérez, P., Bouthemy, P.: Sonar image segmentation using an unsupervised hierarchical MRF model. IEEE Trans. on Image Processing 9(7), 1216–1231 (2000)CrossRefGoogle Scholar
  2. 2.
    Mignotte, M., Meunier, J., Tardif, J.-C.: Endocardial boundary estimation and tracking in echocardiographic images using deformable templates and markov random fields. Pattern Analysis and Applications 4(4), 256–271 (2001)CrossRefMathSciNetzbMATHGoogle Scholar
  3. 3.
    Mignotte, M., Meunier, J.: A multiscale optimization approach for the dynamic contour-based boundary detection issue. Computerized Medical Imaging and Graphics 25(3), 265–275 (2001)CrossRefGoogle Scholar
  4. 4.
    Destrempes, F., Mignotte, M.: Localization of shapes using statistical models and stochastic optimization. IEEE Trans. on Pattern Analysis and Machine Intelligence 29(9), 1603–1615 (2007)Google Scholar
  5. 5.
    Artaechevarria, X., Muñoz-Barrutia, A., Ortiz-de-Solorzano, C.: Combination strategies in multi-atlas image segmentation: Application to brain MR data. IEEE Trans. Med. Imaging 28(8), 1266–1277 (2009)CrossRefGoogle Scholar
  6. 6.
    Aljabar, P., Heckemann, R.A., Hammers, A., Hajnal, J.V., Rueckert, D.: Multi-atlas based segmentation of brain images: Atlas selection and its effect on accuracy. NeuroImage, 726–738 (2009)Google Scholar
  7. 7.
    Dowling, J.A., Fripp, J., Chandra, S., Pluim, J.P.W., Lambert, J., Parker, J., Denham, J., Greer, P.B., Salvado, O.: Fast automatic multi-atlas segmentation of the prostate from 3D MR images. In: Madabhushi, A., Dowling, J., Huisman, H., Barratt, D. (eds.) Prostate Cancer Imaging 2011. LNCS, vol. 6963, pp. 10–21. Springer, Heidelberg (2011) Google Scholar
  8. 8.
    Morin, J.-P., Desrosiers, C., Duong, L.: A random walk approach for multiatlas-based segmentation. In: ICPR 2012, pp. 3636–3639 (2012)Google Scholar
  9. 9.
    Rohlfing, T., Brandt, R., Menzel, R., Maurer, C.R.: Evaluation of atlas selection strategies for atlas-based image segmentation with application to confocal microscopy images of bee brains. Neuroimage 21(4), 1428–1442 (2004)CrossRefGoogle Scholar
  10. 10.
    Rohlfing, T., Russakoff, D.B., Maurer, C.R.: Performance-based classifier combination in atlas-based image segmentation using expectation-maximization parameter estimation. IEEE Trans. Med. Imaging 23(8), 983–994 (2004)CrossRefGoogle Scholar
  11. 11.
    Mignotte, M.: A label field fusion model with a variation of information estimator for image segmentation. Information Fusion 20, 7–20 (2014)CrossRefGoogle Scholar
  12. 12.
    Mignotte, M., Meunier, J., Soucy, J.-P.: DCT-based complexity regularization for EM tomographic reconstruction. IEEE Trans. on Biomedical Engineering 55(2), 801–805 (2008)CrossRefGoogle Scholar
  13. 13.
    Yu, G., Sapiro, G.: DCT image denoising: a simple and effective image denoising algorithm. Image Processing On Line 1 (2011)Google Scholar
  14. 14.
    Canny, J.: A computational approach to edge detection. IEEE Transactions on Pattern Analysis and Machine Intelligence 8(6), 679–698 (1986)Google Scholar
  15. 15.
    Benameur, S., Mignotte, M., Parent, S., Labelle, H., Skalli, W., De Guise, J.: 3d/2d registration and segmentation of scoliotic vertebrae using statistical models. Computerized Medical Imaging and Graphics 27(5), 321–327 (2003)CrossRefGoogle Scholar
  16. 16.
    Ren, X., Malik, J.: Learning a classification model for segmentation. In: 9th IEEE International Conference on Computer Vision, vol. 1, pp. 10–17 (October 2003)Google Scholar
  17. 17.
    Jodoin, P.-M., Mignotte, M., Rosenberger, C.: Segmentation framework based on label field fusion. IEEE Trans. on Image Processing 16(10), 2535–2550 (2007)CrossRefMathSciNetGoogle Scholar
  18. 18.
    Mignotte, M.: A segmentation-based regularization term for image deconvolution. IEEE Trans. on Image Processing 15(7), 1973–1984 (2006)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Dac Cong Tai Nguyen
    • 2
    • 3
  • Said Benameur
    • 2
    • 3
    Email author
  • Max Mignotte
    • 2
  • Frédéric Lavoie
    • 1
    • 3
  1. 1.Orthopedic Surgery DepartmentCentre Hospitalier de l’Université de Montréal (CHUM)MontréalCanada
  2. 2.Département d’Informatique et de Recherche Opérationnelle (DIRO)Université de MontréalQuébecCanada
  3. 3.Eiffel Medtech Inc.MontréalCanada

Personalised recommendations