Histology to μCT Data Matching Using Landmarks and a Density Biased RANSAC

  • Natalia Chicherova
  • Ketut Fundana
  • Bert Müller
  • Philippe C. Cattin
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8673)


The fusion of information from different medical imaging techniques plays an important role in data analysis. Despite the many proposed registration algorithms the problem of registering 2D histological images to 3D CT or MR imaging data is still largely unsolved.

In this paper we propose a computationally efficient automatic approach to match 2D histological images to 3D micro Computed Tomography data. The landmark-based approach in combination with a density-driven RANSAC plane-fitting allows efficient localization of the histology images in the 3D data within less than four minutes (single-threaded MATLAB code) with an average accuracy of 0.25 mm for correct and 2.21 mm for mismatched slices. The approach managed to successfully localize 75% of the histology images in our database. The proposed algorithm is an important step towards solving the problem of registering 2D histology sections to 3D data fully automatically.


Point Cloud Histological Image Histological Slice Histological Cross Section Small Euclidean Distance 
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.


  1. 1.
    Baumberg, A.: Reliable feature matching across widely separated views. In: IEEE Conference on Computer Vision and Pattern Recognition, vol. 1, pp. 774–781 (2000)Google Scholar
  2. 2.
    Bay, H., Ess, A., Tuytelaars, T., Van Gool, L.: Speeded-up robust features (SURF). Computer Vision and Image Understanding 110, 346–359 (2008)CrossRefGoogle Scholar
  3. 3.
    Fischler, M.A., Bolles, R.C.: Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography. Communications of the ACM 24, 381–395 (1981)CrossRefMathSciNetGoogle Scholar
  4. 4.
    Lowe, D.G.: Distinctive image features from scale-invariant keypoints. International Journal of Computer Vision 60, 91–110 (2004)CrossRefGoogle Scholar
  5. 5.
    Mosaliganti, K., Pan, T., Sharp, R., Ridgway, R., Iyengar, S., Gulacy, A., Wenzel, P., de Bruin, A., Machiraju, R., Huang, K., et al.: Registration and 3D visualization of large microscopy images. In: SPIE Medical Imaging, vol. 6144 (2006)Google Scholar
  6. 6.
    Osechinskiy, S., Kruggel, F.: Slice-to-volume nonrigid registration of histological sections to MR images of the human brain. Anatomy Research International (2010)Google Scholar
  7. 7.
    Ou, Y., Shen, D., Feldman, M., Tomaszewski, J., Davatzikos, C.: Non-rigid registration between histological and MR images of the prostate: A joint segmentation and registration framework. In: IEEE Computer Vision and Pattern Recognition Workshops, pp. 125–132 (2009)Google Scholar
  8. 8.
    Sarve, H., Lindblad, J., Johansson, C.B.: Registration of 2D histological images of bone implants with 3D SRμCT volumes. In: Advances in Visual Computing, pp. 1071–1080 (2008)Google Scholar
  9. 9.
    Seise, M., Alhonnoro, T., Kolesnik, M.: Interactive registration of 2D histology and 3D CT data for assessment of radiofrequency ablation treatment. Journal of Pathology Informatics 2, 72 (2011)Google Scholar
  10. 10.
    Stalder, A.K., Ilgenstein, B., Chicherova, N., Deyhle, H., Beckmann, F., Müller, B., Hieber, S.E.: Combined use of micro computed tomography and histology to evaluate the regenerative capacity of bone grafting materials. International Journal of Materials Research (2014)Google Scholar
  11. 11.
    Zitova, B., Flusser, J.: Image registration methods: a survey. Image and Vision Computing 21, 977–1000 (2003)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Natalia Chicherova
    • 1
    • 2
  • Ketut Fundana
    • 1
  • Bert Müller
    • 2
  • Philippe C. Cattin
    • 1
  1. 1.Medical Image Analysis CenterUniversity of BaselBaselSwitzerland
  2. 2.Biomaterials Science CenterUniversity of BaselBaselSwitzerland

Personalised recommendations