Automatic Human Knee Cartilage Segmentation from Multi-contrast MR Images Using Extreme Learning Machines and Discriminative Random Fields

  • Kunlei Zhang
  • Wenmiao Lu
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7009)


Accurate and automatic segmentation of knee cartilage is required for the quantitative cartilage measures and is crucial for the assessment of acute injury or osteoarthritis. Unfortunately, the current works are still unsatisfactory. In this paper, we present a novel solution toward the automatic cartilage seg-mentation from multi-contrast magnetic resonance (MR) images using a pixel classification approach. Most of the previous classification based works for cartilage segmentation only rely on the labeling by a trained classifier, such as support vector machines (SVM) or k-nearest neighbor, but they do not consider the spatial interaction. Extreme learning machines (ELM) have been proposed as the training algorithm for the generalized single-hidden layer feedforward networks, which can be used in various regression and classification applica-tions. Works on ELM have shown that ELM for classification not only tends to achieve good generalization performance, but also is easy to be implemented since ELM requires less human intervention (only one user-specified parameter needs to be chosen) and can get direct least-square solution. To incorporate spatial dependency in classification, we propose a new segmentation method based on the convex optimization of an ELM-based association potential and a discriminative random fields (DRF) based interaction potential for segmenting cartilage automatically with multi-contrast MR images. Our method not only benefits from the good generalization classification performance of ELM but also incorporates the spatial dependencies in classification. We test the pro-posed method on multi-contrast MR datasets acquired from 11 subjects. Experimental results show that our method outperforms the classifiers based solely on DRF, SVM or ELM in segmentation accuracy.


Support Vector Machine Extreme Learning Machine Hide Node Automatic Segmentation Conditional Random Field 
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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  1. 1.
    Glocker, B., Komodakis, N., Paragios, N., Glaser, C., Tziritas, G., Navab, N.: Primal/Dual Linear Programming and Statistical Atlases for Cartilage Segmentation. In: Ayache, N., Ourselin, S., Maeder, A. (eds.) MICCAI 2007, Part II. LNCS, vol. 4792, pp. 536–543. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  2. 2.
    Fripp, J., Crozier, S., Warfield, S.K., Ourselin, S.: Automatic Segmentation and Quantitative Analysis of the Articular Cartilages from Magnetic Resonance Images of the Knee. IEEE Trans. Med. Imag. 29(1), 55–64 (2010)CrossRefGoogle Scholar
  3. 3.
    Dodin, P., Pelletier, J., Martel-Pelletier, J., Abram, F.: Automatic Human Knee Cartilage Segmentation from 3-D Magnetic Resonance Images. IEEE Trans. Biomed. Eng. 57(11), 2699–2711 (2010)CrossRefGoogle Scholar
  4. 4.
    Heimann, T., Morrison, B.J., Styner, M.A., Niethammer, M., Warfield, S.K.: Segmentation of Knee Images: A Grand Challenge. In: Proc. Medical Image Analysis for the Clinic: A Grand Challenge, pp. 207–214 (2010)Google Scholar
  5. 5.
    Vincent, G., Wolstenholme, C., Scott, I., Bowes, M.: Fully Automatic Segmentation of the Knee Joint using Active Appearance Models. In: Proc. Medical Image Analysis for the Clinic: A Grand Challenge, pp. 224–230 (2010)Google Scholar
  6. 6.
    Folkesson, J., Dam, E.B., Olsen, O.F., Pettersen, P.C., Christiansen, C.: Segmenting Articular Cartilage Automatically Using a Voxel Classification Approach. IEEE Trans. Med. Imag. 26(1), 106–115 (2007)CrossRefGoogle Scholar
  7. 7.
    Koo, S., Alto, P., Hargreaves, B.A., Gold, G.E.: Automatic Segmentation of Articular Cartilage from MRI. Patent, US 2009/0306496 (2009)Google Scholar
  8. 8.
    Kumar, S., Hebert, M.: Discriminative Random Fields. Int. J. of Comp. Vision 68(2), 179–201 (2006)CrossRefGoogle Scholar
  9. 9.
    Huang, G.B., Zhu, Q.Y., Siew, C.K.: Extreme Learning Machine: Theory and Applications. Neurocomputing 70, 489–501 (2006)CrossRefGoogle Scholar
  10. 10.
    Huang, G.B., Wang, D., Lan, Y.: Extreme Learning Machines: A Survey. Int. J. of Machine Leaning and Cybernetics 2(2), 107–122 (2011), ELM website, CrossRefGoogle Scholar
  11. 11.
    Lafferty, J., Pereira, F., McCallum, A.: Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data. In: Proc. Int. Conf. on Machine Learning, pp. 282–289 (2001)Google Scholar
  12. 12.
    Murphy, K.P., Weiss, Y., Jordan, M.I.: Loopy Belief Propagation for Approximate Inference: an Empirical Study. In: Foo, N.Y. (ed.) AI 1999. LNCS, vol. 1747, pp. 467–475. Springer, Heidelberg (1999)CrossRefGoogle Scholar
  13. 13.
  14. 14.
    García, C., Moreno, J.A.: Kernel Based Method for Segmentation and Modeling of Magnetic Resonance Images. In: Lemaître, C., Reyes, C.A., González, J.A. (eds.) IBERAMIA 2004. LNCS (LNAI), vol. 3315, pp. 636–645. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  15. 15.
    Lee, C., Wang, S., Brown, M., Murtha, A., Greiner, R.: Segmenting Brain Tumors Using Pseudo–Conditional Random Fields. In: Metaxas, D., Axel, L., Fichtinger, G., Székely, G. (eds.) MICCAI 2008, Part I. LNCS, vol. 5241, pp. 359–366. Springer, Heidelberg (2008)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Kunlei Zhang
    • 1
  • Wenmiao Lu
    • 1
  1. 1.School of Electrical & Electronic EngineeringNanyang Technological UniversitySingapore

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