Nature Inspired Phenotype Analysis with 3D Model Representation Optimization

Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 237)

Abstract

In biology 3D models are made to correspond with true nature so as to use these models for a precise analysis. The visualization of these models helps in the further understanding and conveying of the research questions. Here we use 3D models in gaining understanding on branched structures. To that end we will make use of L-systems and will attempt to use the results of our analysis for the gaining of understanding of these L-systems. To perform our analysis we will have to optimize the 3D models. There are lots of different methods to produce such 3D model. For the study of micro-anatomy, however, the possibilities are limited. In planar sampling, the resolution in the sampling plane is higher than the planes perpendicular to the sampling plane. Consequently, 3D models are under sampled along, at least, one axis. In this paper we present a pipeline for reconstruction of a stack of images. We devised a method to convert the under sampled stack of contours into a uniformly distributed point cloud. The point cloud as a whole is integrated in construction of a surface that accurately represents the shape. In the pipeline the 3D dataset is processed and its quality gradually upgraded so that accurate features can be extracted from under sampled dataset.

The optimized 3D models are used in the analysis of phenotypical differences originating from experimental conditions by extracting related shape features from the model. We use two different sets of 3D models. We investigate the lactiferous duct of newborn mice to gain understanding of environmental directed branching. We consider that the lactiferous duct has an innate blue-print of its arborazation and assume this blue-print is kind of encoded in an innate L-system. We analyze the duct as it is exposed to different environmental conditions and reflect on the effect on the innate L-system. In order to make sure we can extract the branch structure in the right manner we analyze 3D models of the zebrafish embryo; these are simpler compared to the lactiferous duct and will ensure us that measuring features can result in the separation of different treatments on the basis of differences in the phenotype.

Our system can deal with the complex 3D models, the features separate the experimental conditions. The results provide a means to reflect on the manipulation of an L-system through external factors.

Keywords

biological model 3D reconstruction phenotype measurement L-System 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Akgül, C.B., Sankur, B., Yemez, Y., Schmitt, F.: 3D Model Retrieval Using Probability Density-Based Shape Descriptors. IEEE Trans. Pattern Anal. Mach. Intell. 31(6), 1117–1133 (2009)CrossRefGoogle Scholar
  2. 2.
    Barrett, W., Mortensen, E., Taylor, D.: An Image Space Algorithm for Morphological Contour Interpolation. In: Proc. Graphics Interface, pp. 16–24 (1994)Google Scholar
  3. 3.
    Boissonnat, J.-D.: Shape reconstruction from planar cross sections. Comput. Vision Graph. Image Process. 44(1), 1–29 (1988)CrossRefGoogle Scholar
  4. 4.
    Braude, I., Marker, J., Museth, K., Nissanov, J., Breen, D.: Contour-based surface reconstruction using implicit curve fitting, and distance field filtering and interpolation. In: Proc. International Workshop on Volume Graphics, pp. 95–102 (2006)Google Scholar
  5. 5.
    Cao, L., Verbeek, F.J.: Evaluation of algorithms for point cloud surface reconstruction through the analysis of shape parameters. In: Proceedings SPIE: 3D Image Processing (3DIP) and Applications 2012, pp. 82900G–82900G–10. SPIE Bellingham, WA (2012)Google Scholar
  6. 6.
    Fritsch, F.N., Carlson, R.E.: Monotone Piecewise Cubic Interpolation. SIAM Journal on Numerical Analysis 17(2), 238–246 (1980)MathSciNetCrossRefMATHGoogle Scholar
  7. 7.
    Herman, G.T., Zheng, J., Bucholtz, C.A.: Shape-Based Interpolation. IEEE Comput. Graph. Appl. 12(3), 69–79 (1992)CrossRefGoogle Scholar
  8. 8.
    Hoppe, H., DeRose, T., Duchamp, T., McDonald, J., Stuetzle, W.: Surface reconstruction from unorganized points. SIGGRAPH Comput. Graph. 26(2), 71–78 (1992)CrossRefGoogle Scholar
  9. 9.
    Kazhdan, M., Bolitho, M., Hoppe, H.: Poisson surface reconstruction. In: SGP 2006: Proceedings of the Fourth Eurographics Symposium on Geometry Processing, Aire-la-Ville, Switzerland, Switzerland, pp. 61–70. Eurographics Association (2006)Google Scholar
  10. 10.
    Keppel, E.: Approximating complex surfaces by triangulation of contour lines. IBM J. Res. Dev. 19(1), 2–11 (1975)MathSciNetCrossRefMATHGoogle Scholar
  11. 11.
    Levin, D.: Multidimen. In: Algorithms for Approximation, pp. 421–431. Clarendon Press, New York (1987)Google Scholar
  12. 12.
    Liu, J., Subramanian, K.: Accurate and robust centerline extraction from tubular structures in medical images 251, 139–162 (2009)Google Scholar
  13. 13.
    Long, F., Zhou, J., Peng, H.: Visualization and analysis of 3D microscopic images. PLoS Computational Biology 8(6), e1002519 (2012)Google Scholar
  14. 14.
    Massey, F.J.: The {K}olmogorov-{S}mirnov Test for Goodness of Fit. Journal of the American Statistical Association 46(253), 68–78 (1951)CrossRefMATHGoogle Scholar
  15. 15.
    Ng, A., Brock, K.K., Sharpe, M.B., Moseley, J.L., Craig, T., Hodgson, D.C.: Individualized 3D reconstruction of normal tissue dose for patients with long-term follow-up: a step toward understanding dose risk for late toxicity. International Journal of Radiation Oncology, Biology, Physics 84(4), e557–e563 (2012)Google Scholar
  16. 16.
    Nilsson, O., Breen, D.E., Museth, K.: Surface Reconstruction Via Contour Metamorphosis: An Eulerian Approach With Lagrangian Particle Tracking. In: 16th IEEE Visualization Conference (VIS 2005), October 23-28. IEEE Computer Society, Minneapolis (2005)Google Scholar
  17. 17.
    Piccinelli, M., Veneziani, A., Steinman, D.A., Remuzzi, A., Antiga, L.: A Framework for Geometric Analysis of Vascular Structures: Application to Cerebral Aneurysms. IEEE Trans. Med. Imaging 28(8), 1141–1155 (2009)CrossRefGoogle Scholar
  18. 18.
    Pressley, A.: Elementary differential geometry. Springer, London (2010)CrossRefMATHGoogle Scholar
  19. 19.
    Rozenberg, G., Salomaa, A. (eds.): The book of L. Springer-Verlag New York, Inc., New York (1986)MATHGoogle Scholar
  20. 20.
    Rozenberg, G., Salomaa, A.: Mathematical Theory of L Systems. Academic Press, Inc., Orlando (1980)MATHGoogle Scholar
  21. 21.
    Rübel, O., Weber, G.H., Huang, M.-Y., Wes Bethel, E., Biggin, M.D., Fowlkes, C.C., Luengo Hendriks, C.L., Keränen, S.V.E., Eisen, M.B., Knowles, D.W., Malik, J., Hagen, H., Hamann, B.: Integrating data clustering and visualization for the analysis of 3D gene expression data. IEEE/ACM Transactions on Computational Biology and Bioinformatics/IEEE 7(1), 64–79 (2010)CrossRefGoogle Scholar
  22. 22.
    Verbeek, F.J., Huijsmans, D.P.: A graphical database for 3D reconstruction supporting (4) different geometrical representations 465, 117–144 (1998)Google Scholar
  23. 23.
    Verbeek, F.J., Huijsmans, D.P., Baeten, R.J.A.M., Schoutsen, N.J.C., Lamers, W.H.: Design and implementation of a database and program for 3D reconstruction from serial sections: A data-driven approach. Microscopy Research and Technique 30(6), 496–512 (1995)CrossRefGoogle Scholar
  24. 24.
    Wadell, H.: Volume, shape, and roundness of quartz particles. The Journal of Geology 43(3), 250–280 (1935)CrossRefGoogle Scholar
  25. 25.
    Zhang, C., Chen, T.: Efficient Feature Extraction for 2D/3D Objects in Mesh Representation. In: Mesh Representation&quot, ICIP 2001, pp. 935–938 (2001)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  1. 1.Section Imaging & BioInformatics, Leiden Institute of Advanced Computer ScienceLeiden UniversityLeidenThe Netherlands

Personalised recommendations