Advertisement

Detection of Local Intensity Changes in Grayscale Images with Robust Methods for Time-Series Analysis

  • Sermad AbbasEmail author
  • Roland Fried
  • Ursula Gather
Chapter
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9580)

Abstract

The purpose of this paper is to automatically detect local intensity changes in time series of grayscale images. For each pixel coordinate, a time series of grayscale values is extracted. An intensity change causes a jump in the level of the time series and affects several adjacent pixel coordinates at almost the same points in time. We use two-sample tests in moving windows to identify these jumps. The resulting candidate pixels are aggregated to segments using their estimated jump time and coordinates. As an application we consider data from the plasmon assisted microscopy of nanosize objects to identify specific particles in a sample fluid. Tests based on the one-sample Hodges-Lehmann estimator, the two-sample t-test or the two-sample Wilcoxon rank-sum test achieve high detection rates and a rather precise estimation of the change time.

Keywords

Change points Jump detection Spatio-temporal analysis Image sequences 

Notes

Acknowledgments

The work on this paper has been supported by Deutsche Forschungsgemeinschaft (DFG) within the Collaborative Research Center SFB 876 “Providing Information by Resource-Constrained Analysis”, project C3. The computations were performed on the LiDO HPC cluster at TU Dortmund University. We thank the Leibniz-Institut für Analytische Wissenschaften – ISAS – e.V. in Dortmund for providing the PAMONO data to us and Dominic Siedhoff and Pascal Libuschewski for helpful advice on how to work with the PAMONO data and for providing the results for the reference method.

References

  1. 1.
    Abbas, S.: Detektion von Nanoobjekten in Graustufenbildern und Bildsequenzen mittels robuster Zeitreihenmethoden zur Strukturbrucherkennung. Master’s thesis, TU Dortmund University, Dortmund (2013)Google Scholar
  2. 2.
    Bischl, B., Lang, M., Mersmann, O.: BatchExperiments: Statistical Experiments on Batch Computing Clusters. R package version 1.0-0968 (2013)Google Scholar
  3. 3.
    Bivand, R.S., Pebesma, E., Gomez-Rubio, V.: Applied Spatial Data Analysis with R, 2nd edn. Springer, New York (2013)CrossRefzbMATHGoogle Scholar
  4. 4.
    Dahl, D.B.: xtable: Export tables to LaTeX or HTML. R package version 1.7-3 (2014)Google Scholar
  5. 5.
    Fried, R.: On the Robust detection of edges in time series filtering. Comput. Stat. Data Anal. 52(2), 1063–1074 (2007)MathSciNetCrossRefzbMATHGoogle Scholar
  6. 6.
    Fried, R., Dehling, H.: Robust nonparametric tests for the two-sample location problem. Stat. Methods Appl. 20(4), 409–422 (2011)MathSciNetCrossRefzbMATHGoogle Scholar
  7. 7.
    Fried, R., Gather, U.: On rank tests for shift detection in time series. Comput. Stat. Data Anal. 52(1), 221–233 (2007)MathSciNetCrossRefzbMATHGoogle Scholar
  8. 8.
    Gomes, A.J.P., Voiculescu, I., Jorge, J., Wyvill, B., Galbraith, C.: Implicit Curves and Surfaces: Mathematics, Data Structures and Algorithms. Springer, Dordrecht (2009)CrossRefzbMATHGoogle Scholar
  9. 9.
    Gonzalez, R.C., Woods, R.E.: Digital Image Processing, 3rd edn. Prentice Hall, Upper Saddle River (2008)Google Scholar
  10. 10.
    Hodges, J.L., Lehmann, E.L.: Estimates of location based on rank tests. Ann. Math. Stat. 34(2), 598–611 (1963)MathSciNetCrossRefzbMATHGoogle Scholar
  11. 11.
    Holm, S.: A simple sequentially rejective multiple test procedure. Scand. J. Stat. 6(2), 65–70 (1979)MathSciNetzbMATHGoogle Scholar
  12. 12.
    Høyland, A.: Robustness of the Hodges-Lehmann estimates for shift. Ann. Math. Statist. 36(1), 174–197 (1965)MathSciNetCrossRefzbMATHGoogle Scholar
  13. 13.
    Jähne, B.: Digitale Bildverarbeitung, 7th edn. Springer, Berlin (2012)CrossRefzbMATHGoogle Scholar
  14. 14.
    Maronna, R.A., Martin, R.D., Yohai, V.J.: Robust Statistics: Theory and methods. Wiley Series in Probability and Statistics. Wiley, Chichester (2006)CrossRefzbMATHGoogle Scholar
  15. 15.
    Meyer, D., Dimitriadou, E., Hornik, K., Weingessel, A., Leisch, F.: e1071: Misc Functions of the Department of Statistics (e1071), TU Wien. R package version 1.6-3 (2014)Google Scholar
  16. 16.
    Morell, O.: On nonparametric methods for robust jump-preserving smoothing and trend detection. Ph.D. thesis, TU Dortmund University, Dortmund (2012)Google Scholar
  17. 17.
    Pages, H., Carlson, M., Falcon, S., Li, N.: AnnotationDbi: Annotation Database Interface. R package version 1.26.0Google Scholar
  18. 18.
    Qiu, P., Yandell, B.: A local polynomial jump-detection algorithm in nonparametric regression. Technometrics 40(2), 141–152 (1998)MathSciNetGoogle Scholar
  19. 19.
    R Core Team: R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna (2013)Google Scholar
  20. 20.
    Rangayyan, R.M.: Biomedical Image Analysis. CRC Press, Boca Raton (2005)Google Scholar
  21. 21.
    Sharpsteen, C., Bracken, C.: tikzDevice: R Graphics Output in LaTeX Format. R package version 0.7.0 (2013)Google Scholar
  22. 22.
    Siedhoff, D., Weichert, F., Libuschewski, P., Timm, C.: Detection and classification of nano-objects in biosensor data. In: International Conference on Systems Biology - Microscopic Image Analysis with Application in Biology (MIAAB 2011), pp. 1–6 (2011)Google Scholar
  23. 23.
    Timm, C., Libuschewski, P., Siedhoff, D., Weichert, F., Müller, H., Marwedel, P.: Improving nanoobject detection in optical biosensor data. In: Proceedings of the 5th International Symposium on Bio- and Medical Information and Cybernetics (BMIC 2011), pp. 236–240 (2011)Google Scholar
  24. 24.
    Urbanek, S.: png: Read and Write PNG Images. R package version 0.1-7 (2013)Google Scholar
  25. 25.
    Weichert, F., Gaspar, M., Timm, C., Zybin, A., Gurevich, E.L., Engel, M., Müller, H., Marwedel, P.: Signal analysis and classification for surface plasmon assisted microscopy of nanoobjects. Sens. Actuators B 151, 281–290 (2010)CrossRefGoogle Scholar
  26. 26.
    Wickham, H.: ggplot2: Elegant Graphics for Data Analysis. Springer, New York (2009)CrossRefzbMATHGoogle Scholar
  27. 27.
    Wu, J.S., Chu, C.K.: Kernel-type estimators of jump points and values of a regression function. Ann. Stat. 21(3), 1545–1566 (1993)MathSciNetCrossRefzbMATHGoogle Scholar
  28. 28.
    Zybin, A., Kuritsyn, Y.A., Gurevich, E.L., Temchura, V.V., Überla, K., Niemax, K.: Real-time detection of single immobilized nanoparticles by surface plasmon resonance Imaging. Plasmonics 5(1), 31–35 (2010)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  1. 1.Department of StatisticsTU Dortmund UniversityDortmundGermany

Personalised recommendations