Advertisement

Statistical Analysis of the Relationship between Spots and Structures in Microscopy Images

  • Susanne Schaller
  • Jaroslaw Jacak
  • Rene Silye
  • Stephan M. Winkler
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8111)

Abstract

Fluorescence microscopy image analysis plays an important role in biomedical diagnostics and is an essential approach for researching and investigating the development and state of various diseases. In this paper we describe an approach for analyzing nanoscale microscopy images in which spots and background structures are identified and their relationship is quantified. A spatial analysis approach is used for identifying spots, then clustering of these spots is performed and those clusters are characterized using a series of here defined features. These cluster characteristics are used for comparing images via statistical hypothesis tests (using the Kolmogorov-Smirnov test for the equality of probability distributions). Moreover, to achieve a better distinction we additionally define features that quantify the relationship of clusters of spots and background structures. In the empirical section we demonstrate the use of this approach in the analysis of microscopy images of brain structures of patients potentially suffering from a neural disease (e.g., depression or schizophrenia). Using the here presented approach we will be able to investigate the development and state of various diseases in a better way and help to find more systematic medication of diseases in the future.

Keywords

Microscopy Image Cluster Characteristic Cluster Distance Statistical Hypothesis Testing Background Structure 
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.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Comaniciu, D., Meer, P.: Robust analysis of feature spaces: color image segmentation. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 750–755 (1997)Google Scholar
  2. 2.
    van de Linde, S., Löschberger, A., Klein, T., Heidbreder, M., Wolter, S., Heilemann, M., Sauer, M.: Direct stochastic optical reconstruction microscopy with standard fluorescent probes. Nature Protocols 6(7), 991–1009 (2011)CrossRefGoogle Scholar
  3. 3.
    MacKay, D.: Information Theory, Inference and Learning Algorithms, pp. 284–292. Cambridge University Press (2003)Google Scholar
  4. 4.
    Massey Jr., F.J.: The Kolmogorov-Smirnov test for goodness of fit. Journal of the American Statistical Association 46, 68–78 (1951)CrossRefzbMATHGoogle Scholar
  5. 5.
    Muresan, L., Jacak, J., Klement, E., Hesse, J., Schütz, G.: Microarray analysis at single-molecule resolution. IEEE Transactions on NanoBioscience 9(1), 51–58 (2010)CrossRefGoogle Scholar
  6. 6.
    Olivo-Marin, J.: Extraction of spots in biological images using multiscale products. Pattern Recognition 35, 1989–1996 (2002)CrossRefzbMATHGoogle Scholar
  7. 7.
    Patterson, G., Davidson, M., Manley, S., Lippincott-Schwartz, J.: Superresolution imaging using single-molecule localization. Annual Review of Physical Chemistry 61, 345–367 (2010), PMID: 20055680 CrossRefGoogle Scholar
  8. 8.
    Ripley, B.D.: Spatial statistics, vol. 575. Wiley-Interscience (2005)Google Scholar
  9. 9.
    Schmidt, R., Jacak, J., Schirwitz, C., Stadler, V., Michel, G., Marmé, N., Schütz, G.J., Hoheisel, J.D., Knemeyer, J.: Single-molecule detection on a protein-array assay platform for the exposure of a tuberculosis antigen. Journal of Proteome Research 10(3), 1316–1322 (2011)CrossRefGoogle Scholar
  10. 10.
    Zhang, Y., Brady, M., Smith, S.: Segmentation of brain MR images through a hidden Markov random field model and the expectation-maximization algorithm. IEEE Transactions on Medical Imaging 20(1), 45–57 (2001)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Susanne Schaller
    • 1
  • Jaroslaw Jacak
    • 2
  • Rene Silye
    • 3
  • Stephan M. Winkler
    • 1
  1. 1.Bioinformatics Research GroupUniversity of Applied Sciences Upper AustriaHagenbergAustria
  2. 2.Institute for Applied PhysicsJohannes Kepler University LinzLinzAustria
  3. 3.Department of NeurosurgeryWagner-Jauregg HospitalLinzAustria

Personalised recommendations