Improved Two-Level Model Averaging Techniques in Drosophila Brain Modeling

  • Cheng-Chi Wu
  • Chao-Yu Chen
  • Hsiu-Ming Chang
  • Ann-Shyn Chiang
  • Yung-Chang Chen
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5414)


Two-level model averaging techniques have been proposed to construct the 3D reference template for the Drosophila brain. The surface-based reference template is suitable for integration of experimental data from different laboratories. The 3D distance transform is the most memory and time consuming part in the model averaging algorithm. With the improvement of microscopic scanning technology, images of higher resolution can be acquired. Thus, the memories required for 3D distance transform become critical. In this paper, improved two-level model averaging techniques are proposed with three improvements. A two-scale distance map creation algorithm is introduced to reduce the memory cost in the distance transform. The computational time is reduced by a reduction of computation points in the distance map creation. The third improvement is an outlier rejection module to improve the robustness of the resulting average model.


Model averaging Drosophila surface-based 


  1. 1.
    Talairach, J., Tournoux, P.: Coplanar Stereotaxic Atlas of the Human Brain. Thieme Medical, New York (1988)Google Scholar
  2. 2.
    Friston, K.J., Holmes, A.P., Worsley, K.J., Poline, J.P., Frith, C.D., Frackowiak, R.S.J.: Statistical Parametric Maps in Functional Imaging: A general Linear Approach. Human Brain Mapping 2, 189–210 (1995)CrossRefGoogle Scholar
  3. 3.
    Brandt, R., Rohlfing, T., Rybak, J., Krofczik, S., Maye, A., Westerhoff, M., Hege, H.-C., Menzel, R.: Three-Dimensional Average-Shape Atlas of the Honeybee Brain and its Applications. J. Comp. Neurol. 492, 1–19 (2005)CrossRefGoogle Scholar
  4. 4.
    Rein, K., Zockler, M., Mader, M.T., Grubel, C., Heisenberg, M.: The Drosophila Standard Brain. Current Biology 12, 227–231 (2002)CrossRefGoogle Scholar
  5. 5.
    Chen, Y.C., Chen, Y.C., Chiang, A.S.: Two-Level Model Averaging Techniques in Dorsophila Brain Imaging. In: Proceedings of 2002 IEEE International Conference on Image Processing, vol. 2, pp. 941–944. Rochester, New York (2002)Google Scholar
  6. 6.
    Maurer, C.R., Qi, R., Raghavan, V.: A linear time algorithm for computing exact Euclidean distance transforms of binary images in arbitrary dimensions. IEEE Trans. on Pattern Analysis and Machine Intelligence 25, 265–270 (2003)CrossRefGoogle Scholar
  7. 7.
    Dachille, F., Kaufman, A.: Incremental Triangle Voxelization. In: Proc. Graphics Interface, pp. 205–212 (2000)Google Scholar
  8. 8.
    Baerentzen, J.A., Aanaes, H.: Signed Distance Computation Using the Angle Weighted Pseudonormal. IEEE Trans. on Visualization and Computer Graphics 11(3), 243–253 (2005)CrossRefGoogle Scholar
  9. 9.
    Gue’ziec, A.: Meshsweeper: Dynamic Point-to-Polygonal Mesh Distance and Applications. IEEE Trans. on Visualization and Computer Graphics 7(1), 47–60 (2001)CrossRefGoogle Scholar
  10. 10.
    Chen, Y.C., Chen, Y.C., Chiang, A.S., Hsieh, K.S.: A Reliable Surface Reconstruction System in Biomedicine. Computer Methods and Programs in Biomedicine 86(2), 141–152 (2007)CrossRefGoogle Scholar
  11. 11.
    Rohlfing, T., Maurer, C.R.: Shape-Based Averaging. IEEE Trans. on Image Proc. 16(1), 153–161 (2007)MathSciNetCrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Cheng-Chi Wu
    • 1
  • Chao-Yu Chen
    • 1
  • Hsiu-Ming Chang
    • 2
  • Ann-Shyn Chiang
    • 2
  • Yung-Chang Chen
    • 1
  1. 1.Department of Electrical EngineeringTaiwan, R.O.C.
  2. 2.Department of Life ScienceNational Tsing Hua UniversityHsinchuTaiwan, R.O.C.

Personalised recommendations