Mixed Metric Random Forest for Dense Correspondence of Cone-Beam Computed Tomography Images

  • Yuru PeiEmail author
  • Yunai Yi
  • Gengyu Ma
  • Yuke Guo
  • Gui Chen
  • Tianmin Xu
  • Hongbin Zha
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10433)


Efficient dense correspondence and registration of CBCT images is an essential yet challenging task for inter-treatment evaluations of structural variations. In this paper, we propose an unsupervised mixed metric random forest (MMRF) for dense correspondence of CBCT images. The weak labeling resulted from a clustering forest is utilized to discriminate the badly-clustered supervoxels and related classes, which are favored in the following fine-tuning of the MMRF by penalized weighting in both classification and clustering entropy estimation. An iterative scheme is introduced for the forest reinforcement to minimize the inconsistent supervoxel labeling across CBCT images. In order to screen out the inconsistent matching pairs and to regularize the dense correspondence defined by the forest-based metric, we evaluate consistencies of candidate matching pairs by virtue of isometric constraints. The proposed correspondence method has been tested on 150 clinically captured CBCT images, and outperforms state-of-the-arts in terms of matching accuracy while being computationally efficient.



This work was supported by National Natural Science Foundation of China under Grant 61272342, and the Seeding Grant for Medicine and Information Sciences of Peking University under Grant 2014MI24.


  1. 1.
    Achanta, R., Shaji, A., Smith, K., Lucchi, A., Fua, P., Süsstrunk, S.: SLIC superpixels compared to state-of-the-art superpixel methods. IEEE Trans. PAMI 34(11), 2274–2282 (2012)CrossRefGoogle Scholar
  2. 2.
    Bhagalia, R., Fessler, J.A., Kim, B.: Accelerated nonrigid intensity-based image registration using importance sampling. IEEE Trans. MI 28(8), 1208–1216 (2009)CrossRefGoogle Scholar
  3. 3.
    Breiman, L.: Random forests. Mach. Learn. 45(1), 5–32 (2001)CrossRefGoogle Scholar
  4. 4.
    Cevidanes, L.H., Motta, A., Proffit, W.R., Ackerman, J.L., Styner, M.: Cranial base superimposition for 3-dimensional evaluation of soft-tissue changes. Am. J. Orthod. Dentofac. Orthoped. 137(4), S120–S129 (2010)CrossRefGoogle Scholar
  5. 5.
    Coupé, P., Manjón, J., Fonov, V., Pruessner, J., Robles, M., Collins, D.L.: Patch-based segmentation using expert priors: application to hippocampus and ventricle segmentation. NeuroImage 54, 940–954 (2011)CrossRefGoogle Scholar
  6. 6.
    Criminisi, A., et al.: Decision forests: a unified framework for classification, regression, density estimation, manifold learning and semi-supervised learning. Found. Trends Comput. Graph. Vis. 7(23), 81–227 (2012)zbMATHGoogle Scholar
  7. 7.
    Kanavati, F., Tong, T., Misawa, K., Fujiwara, M., Mori, K., Rueckert, D., Glocker, B.: Supervoxel classification forests for estimating pairwise image correspondences. In: Zhou, L., Wang, L., Wang, Q., Shi, Y. (eds.) MLMI 2015. LNCS, vol. 9352, pp. 94–101. Springer, Cham (2015). doi: 10.1007/978-3-319-24888-2_12CrossRefGoogle Scholar
  8. 8.
    Maes, F., Collignon, A., Vandermeulen, D., Marchal, G., Suetens, P.: Multimodality image registration by maximization of mutual information. IEEE Trans. MI 16(2), 187–198 (1997)CrossRefGoogle Scholar
  9. 9.
    Park, J.H., et al.: 3-dimensional cone-beam computed tomography superimposition: a review. In: Seminars in Orthodontics, vol. 21, pp. 263–273. Elsevier (2015)Google Scholar
  10. 10.
    Pei, Y., Kim, T., Zha, H.: Unsupervised random forest manifold alignment for lipreading. In: IEEE ICCV (2013)Google Scholar
  11. 11.
    Pei, Y., Ma, G., Chen, G., Zhang, X., Xu, T., Zha, H.: Superimposition of cone-beam computed tomography images by joint embedding. IEEE Trans. BME 64, 1218–1227 (2016)CrossRefGoogle Scholar
  12. 12.
    Rodolà, E., Rota Bulo, S., Windheuser, T., Vestner, M., Cremers, D.: Dense non-rigid shape correspondence using random forests. In: CVPR, pp. 4177–4184 (2014)Google Scholar
  13. 13.
    Wang, L., et al.: Automated segmentation of CBCT image using spiral CT atlases and convex optimization. In: Mori, K., Sakuma, I., Sato, Y., Barillot, C., Navab, N. (eds.) MICCAI 2013. LNCS, vol. 8151, pp. 251–258. Springer, Heidelberg (2013). doi: 10.1007/978-3-642-40760-4_32CrossRefGoogle Scholar
  14. 14.
    Zhu, X., Loy, C.C., Gong, S.: Constrained clustering with imperfect oracles. IEEE Trans. NNLS 27(6), 1345–1357 (2016)MathSciNetGoogle Scholar
  15. 15.
    Zikic, D., Glocker, B., Criminisi, A.: Encoding atlases by randomized classification forests for efficient multi-atlas label propagation. Med. Image Anal. 18(8), 1262–1273 (2014)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Yuru Pei
    • 1
    Email author
  • Yunai Yi
    • 1
  • Gengyu Ma
    • 2
  • Yuke Guo
    • 3
  • Gui Chen
    • 4
  • Tianmin Xu
    • 4
  • Hongbin Zha
    • 1
  1. 1.Key Laboratory of Machine Perception (MOE), Department of Machine IntelligencePeking UniversityBeijingChina
  2. 2.uSens Inc.San JoseUSA
  3. 3.Luoyang Institute of Science and TechnologyLuoyangChina
  4. 4.School of StomatologyPeking UniversityBeijingChina

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