Advertisement

3D Segmentation in CT Imagery with Conditional Random Fields and Histograms of Oriented Gradients

  • Chetan Bhole
  • Nicholas Morsillo
  • Christopher Pal
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7009)

Abstract

In this paper we focus on the problem of 3D segmention in volumetric computed tomography imagery to identify organs in the abdomen. We propose and evaluate different models and modeling strategies for 3D segmentation based on traditional Markov Random Fields (MRFs) and their discriminative counterparts known as Conditional Random Fields (CRFs). We also evaluate the utility of features based on histograms of oriented gradients or HOG features. CRFs and HOG features have independently produced state of the art performance in many other problem domains and we believe our work is the first to combine them and use them for medical image segmentation. We construct 3D lattice MRFs and CRFs, use variational message passing (VMP) for learning and max-product (MP) inference for prediction in the models. These inference and learning approaches allow us to learn pairwise terms in random fields that are non-submodular and are thus very flexible. We focus our experiments on abdominal organ and region segmentation, but our general approach should be useful in other settings. We evaluate our approach on a larger set of anatomical structures found within a publicly available liver database and we provide these labels for the dataset to the community for future research.

Keywords

MRF CRF generative discriminative 3D segmentation HOG 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Blake, A., Rother, C., Brown, M., Perez, P., Torr, P.: Interactive image segmentation using an adaptive GMMRF model. In: Pajdla, T., Matas, J(G.) (eds.) ECCV 2004. LNCS, vol. 3021, pp. 428–441. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  2. 2.
    Boykov, Y., Veksler, O., Zabih, R.: Fast approximate energy minimization via graph cuts. IEEE Transactions on Pattern Analysis and Machine Intelligence 23 (2001)Google Scholar
  3. 3.
    Criminisi, A., Shotton, J., Robertson, D.P., Konukoglu, E.: Regression forests for efficient anatomy detection and localization in CT studies. In: MCV, pp. 106–117 (2010)Google Scholar
  4. 4.
    Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: IEEE CVPR, pp. 886–893. IEEE Computer Society, Washington, DC, USA (2005)Google Scholar
  5. 5.
    Graf, F., Kriegel, H.P., Schubert, M., Strukelj, M., Cavallaro, A.: Fully automatic detection of the vertebrae in 2d ct images. In: SPIE Medical Imaging, vol. 7962 (2011)Google Scholar
  6. 6.
    Kumar, S., Hebert, M.: Discriminative random fields: A discriminative framework for contextual interaction in classification. In: ICCV, pp. 1150–1157 (2003)Google Scholar
  7. 7.
    Lafferty, J., McCallum, A., Pereira, F.: Conditional random fields: Probabilistic models for segmenting and labeling sequence data (2001)Google Scholar
  8. 8.
    Lee, C.H., Wang, S., Murtha, A., Brown, M.R.G., 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
  9. 9.
    Ling, H., et al.: Hierarchical, learning-based automatic liver segmentation. In: IEEE CVPR (2008)Google Scholar
  10. 10.
    Lowe, D.G.: Distinctive image features from scale-invariant keypoints. IJCV 60(2) (2004)Google Scholar
  11. 11.
    Motwani, K., Adluru, N., Hinrichs, C., Alexander, A.L., Singh, V.: Epitome driven 3-d diffusion tensor image segmentation: on extracting specific structures. In: NIPS (2010)Google Scholar
  12. 12.
    Seifert, S., et al.: Hier. parsing and semantic nav. of full body CT data. In: Proc. SPIE (2009)Google Scholar
  13. 13.
    Tsechpenakis, G., Wang, J., Mayer, B., Metaxas, D.: Coupling CRFs and deformable models for 3D medical image segmentation, pp. 1–8 (2007)Google Scholar
  14. 14.
    Varshney, L.: Abdominal organ segmentation in ct scan images: A survey (2002)Google Scholar
  15. 15.
    Winn, J., Bishop, C.M.: Variational message passing. J. Mach. Learn. Res. 6, 661–694 (2005)MathSciNetzbMATHGoogle Scholar
  16. 16.
    Zhang, Y., Brady, M., Smith, S.: Segmentation of brain MR images through a hidden markov random field model and the EM algorithm. IEEE Trans. Med. Imaging 20(1), 45–57 (2001)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Chetan Bhole
    • 1
  • Nicholas Morsillo
    • 1
  • Christopher Pal
    • 2
  1. 1.University of RochesterUSA
  2. 2.École Polytechnique de MontréalCanada

Personalised recommendations