3D-Reconstruction of Basal Cell Carcinoma

A Proof-of-Principle Study
  • Patrick Scheibe
  • Tino Wetzig
  • Jens-Peer Kuska
  • Markus Löffler
  • Jan C. Simon
  • Uwe Paasch
  • Ulf-Dietrich Braumann
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6204)


This work presents a complete processing-chain for a 3D-reconstruction of Basal Cell Carcinoma (BCC). BCC is the most common malignant skin cancer with a high risk of local recurrence after insufficient treatment. Therefore, we have focused on the development of an automated image-processing chain for 3D-reconstruction of BCC using large histological serial sections. We introduce a novel kind of image-processing chain (core component: non-linear image registration) which is optimised for the diffuse nature of BCC.

For full-automatic delineation of the tumour within the tissue we apply a fuzzy c-means segmentation method, which does not calculate a hard segmentation decision but class membership probabilities. This feature moves the binary decision tumorous vs. non-tumorous to the end of the processing chain, and it ensures smooth gradients which are needed for a consistent registration.

We used a multi-grid form of the nonlinear registration effectively suppressing registration runs into local minima (possibly caused by diffuse nature of the tumour). To register the stack of images this method is applied in a new way to reduce a global drift of the image stack while registration.

Our method was successfully applied in a proof-of-principle study for automated tissue volume reconstruction followed by a quantitative tumour growth analysis.


Non-linear Image Registration Image Segmentation 3D-Reconstruction 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Raasch, B.A., Buettner, P.G., Garbe, C.: Basal cell carcinoma: histological classification and body-site distribution. Br. J. Dermatol. 155(2), 401–407 (2006)CrossRefGoogle Scholar
  2. 2.
    Rigel, D.S.: Cutaneous ultraviolet exposure and its relationship to the development of skin cancer. J. Am. Acad. Dermatol. 58(5 Suppl. 2), 129–132 (2008)CrossRefGoogle Scholar
  3. 3.
    Woerle, B., Heckmann, M., Konz, B.: Micrographic surgery of basal cell carcinomas of the head. Recent Results Cancer Res. 160, 219–224 (2002)Google Scholar
  4. 4.
    Swanson, N.A.: Mohs surgery. technique, indications, applications, and the future. Arch. Dermatol. 119(9), 761–773 (1983)CrossRefGoogle Scholar
  5. 5.
    Braumann, U.D., Kuska, J.P., Einenkel, J., Horn, L.C., Löffler, M., Höckel, M.: Three-dimensional reconstruction and quantification of cervical carcinoma invasion fronts from histological serial sections. IEEE Transactions on Medical Imaging 24(10), 1286–1307 (2005)CrossRefGoogle Scholar
  6. 6.
    Braumann, U.D., Scherf, N., Einenkel, J., Horn, L.C., Wentzensen, N., Loeffler, M., Kuska, J.P.: Large histological serial sections for computational tissue volume reconstruction. Methods Inf. Med. 46(5), 614–622 (2007)Google Scholar
  7. 7.
    Braun, R., Klumb, F., Bondon, D., Salomon, D., Skaria, A., Adatto, M., French, L., Saurat, J., Vallee, J.: Three-Dimensional Reconstruction of Basal Cell Carcinomas. Derma Surg. 31(5), 562–569 (2005)Google Scholar
  8. 8.
    Matsumura, T., Sato-Matsumura, K.C., Yokota, T., Kobayashi, H., Nagashima, K., Ohkawara, A.: Three-dimensional reconstruction in dermatopathology–a personal computer-based system. J. Cutan. Pathol. 26(4), 197–200 (1999)CrossRefGoogle Scholar
  9. 9.
    Modersitzki, J.: Numerical Methods for Image Registration, Numerical Mathematics and Scientific Computation. Oxford University Press, USA (2004)Google Scholar
  10. 10.
    Chan, T., Osher, S., Shen, J.: The digital TV filter and non-linear denoising (2001)Google Scholar
  11. 11.
    Bezdek, J.: Pattern Recognition with Fuzzy Objective Function algorithms. Plenum, New York (1981)zbMATHGoogle Scholar
  12. 12.
    Dunn, J.C.: A fuzzy relative of the ISODATA process and its use in detecting compact well-separated clusters. Journal of Cybernetics 3, 32–57 (1973)zbMATHCrossRefMathSciNetGoogle Scholar
  13. 13.
    Amit, Y.: A nonlinear variational problem for image matching. SIAM Journal on Scientific Computing 15(1), 207–224 (1994)zbMATHCrossRefMathSciNetGoogle Scholar
  14. 14.
    Braumann, U.D., Kuska, J.P.: Influence of the boundary conditions on the result of non-linear image registration. In: Proceedings of the IEEE International Conference on Image Processing, pp. I-1129–I-1132. IEEE Signal Processing Society, Los Alamitos (September 2005)Google Scholar
  15. 15.
    Choi, S., Wette, R.: Maximum likelihood estimation of the parameters of the gamma distribution and their bias. Technometrics 11(4), 683–690 (1969)zbMATHCrossRefGoogle Scholar
  16. 16.
    Gaffling, S., Jäger, F., Daum, V., Tauchi, M., Lütjen-Drecoll, E.: Interpolation of Histological Slices by means of Non-rigid Registration. In: Meinzer, H.P., Deserno, T.M., Handels, H., Tolxdorff, T. (eds.) Bildverarbeitung für die Medizin 2009 – Algorithmen, Systeme, Anwendungen, Informatik aktuell, pp. 267–271. Springer, Heidelberg (March 2009)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Patrick Scheibe
    • 1
  • Tino Wetzig
    • 2
  • Jens-Peer Kuska
    • 3
  • Markus Löffler
    • 4
  • Jan C. Simon
    • 2
  • Uwe Paasch
    • 2
  • Ulf-Dietrich Braumann
    • 3
  1. 1.Translational Centre for Regenerative Medicine (TRM Leipzig)Universität LeipzigLeipzig
  2. 2.Department of Dermatology, Venerology and AllergologyUniversität LeipzigLeipzig
  3. 3.Interdisciplinary Centre for Bioinformatics (IZBI)Universität LeipzigLeipzigGermany
  4. 4.Institute for Medical Informatics, Statistics, and Epidemiology (IMISE)Universität LeipzigLeipzig

Personalised recommendations