Feedback-Driven Design of Normalization Techniques for Biological Images Using Fuzzy Formulation of a Priori Knowledge

  • Arif ul Maula Khan
  • Markus Reischl
  • Brigitte Schweitzer
  • Carsten Weiss
  • Ralf Mikut
Conference paper
Part of the Studies in Computational Intelligence book series (SCI, volume 445)

Abstract

In digital imaging, a normalization procedure is an important step for an efficient and meaningful analysis of any random image dataset. The original intensity information in a digital image is mostly distorted due to imperfect acquisition conditions resulting in the shading phenomenon. Additionally, the high contrast of gray values present in an image also imparts a bias to retrieved gray values. Consequently, image processing goals such as segmentation and cell classification are adversely affected by aforementioned factors. In many microscopic imaging applications, retrospective shading correction methods are more commonly used as opposed to prospective methods in order to remove unwanted shading effects. The objectives of a normalization process, for one, can be rescaling of pixel values to a desired range while disregarding outliers and noisy background pixels. To counter shading effects, robust normalization techniques based on the adaptation of normalization parameters should be devised. We propose a feedback-based automatic image normalization technique that incorporates the evaluation criterion for its effectiveness based on image processing goals such as segmentation. Such a technique employs surface fitting of the available image pixel values to structures of a given family of function (such as polynomials) describing the spatial intensity variation of that image. It incorporates fuzzy formulation of criteria for normalization evaluation as an internal consistency check, while including post-segmentation results based on a priori segmentation knowledge at the same time. Results from a biological dataset consisting of images showing normal and dying cells are included to elucidate the effectiveness of the proposed scheme by automatically adapting normalization parameters.

Keywords

Image Segmentation Automatic Segmentation Normalization Technique Image Normalization Fuzzy Membership Function 
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.

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References

  1. 1.
    Beller, M., Stotzka, R., Müller, T.: Application of an interactive feature-driven segmentation. Biomedizinische Technik 49(E2), 210–211 (2004)Google Scholar
  2. 2.
    Donauer, J., Schreck, I., Liebel, U., Weiss, C.: Role and interaction of p53, bax and the stress-activated protein kinases p38 and jnk in benzo(a)pyrene-diolepoxide induced apoptosis in human colon carcinoma cells. Archives of Toxicology 86(2), 329–337 (2012)CrossRefGoogle Scholar
  3. 3.
    Farmer, M., Jain, A.: A wrapper-based approach in image segmentation and classification. IEEE Transactions on Image Processing 14(12), 2060–2072 (2005)CrossRefGoogle Scholar
  4. 4.
    Khan, A., Reischl, M., Schweitzer, B., Weiss, C., Mikut, R.: Automatic tuning of image segmentation routines by means of fuzzy feature evaluation. In: Proc., 6th International Conference on Soft Methods in Probability and Statistics, Konstanz, Germany. SCI. Springer (accepted paper, 2012)Google Scholar
  5. 5.
    Likar, B., Maintz, J., Viergever, M., Pernus, F., et al.: Retrospective shading correction based on entropy minimization. Journal of Microscopy 197(3), 285–295 (2000)CrossRefGoogle Scholar
  6. 6.
    Mai, F., Chang, C., Liu, W., Xu, W., Hung, Y.: Segmentation-based retrospective shading correction in fluorescence microscopy E. coli images for quantitative analysis. In: Proc., International Symposium on Multispectral Image Processing and Pattern Recognition, vol 7498, pp. 74,983O–1 (2009)Google Scholar
  7. 7.
    Mikut, R., Burmeister, O., Braun, S., Reischl, M.: The open source Matlab toolbox Gait-CAD and its application to bioelectric signal processing. In: Proc., DGBMT-Workshop Biosignalverarbeitung, Potsdam, pp. 109–111 (2008)Google Scholar
  8. 8.
    Otsu, N.: A threshold selection method from gray-level histograms. IEEE Transactions on Systems, Man and Cybernetics 9, 62–66 (1979)CrossRefGoogle Scholar
  9. 9.
    Reischl, M., Alshut, R., Mikut, R.: On robust feature extraction and classification of inhomogeneous data sets. In: Proc., 20. Workshop Computational Intelligence, Forschungszentrum Karlsruhe, pp. 124–143 (2010)Google Scholar
  10. 10.
    Tomaževič, D., Likar, B., Pernuš, F.: Comparative evaluation of retrospective shading correction methods. Journal of Microscopy 208(3), 212–223 (2002)MathSciNetCrossRefGoogle Scholar
  11. 11.
    Vovk, U., Pernus, F., Likar, B.: A review of methods for correction of intensity inhomogeneity in MRI. IEEE Transactions on Medical Imaging 26(3), 405–421 (2007)CrossRefGoogle Scholar
  12. 12.
    Zwier, J., Van Rooij, G., Hofstraat, J., Brakenhoff, G.: Image calibration in fluorescence microscopy. Journal of Microscopy 216(1), 15–24 (2004)MathSciNetCrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Arif ul Maula Khan
    • 1
  • Markus Reischl
    • 1
  • Brigitte Schweitzer
    • 1
  • Carsten Weiss
    • 1
  • Ralf Mikut
    • 1
  1. 1.Karlsruhe Institute of TechnologyEggenstein-LeopoldshafenGermany

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