Microscopy Image Enhancement for Cost-Effective Cervical Cancer Screening

  • Joakim Lindblad
  • Ewert Bengtsson
  • Nataša Sladoje
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9127)

Abstract

We propose a simple and fast method for microscopy image enhancement and quantitatively evaluate its performance on a database containing cell images obtained from microscope setups of several levels of quality. The method utilizes an efficiently and accurately estimated relative modulation transfer function to generate images of higher quality, starting from those of lower quality, by filtering in the Fourier domain. We evaluate the method visually and based on correlation coefficient and normalized mutual information. We conclude that enhanced images exhibit high similarity, both visually and in terms of information content, with acquired high quality images. This is an important result for the development of a cost-effective screening system for cervical cancer.

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References

  1. 1.
    Blinn, J.: Jim Blinn’s Corner: Dirty Pixels. Morgan Kaufmann Publishers Inc. (1998)Google Scholar
  2. 2.
    Forster, B., Van de Ville, D., Berent, J., Sage, D., Unser, M.: Extended depth-of-focus for multi-channel microscopy images: A complex wavelet approach. In: IEEE Int. Symp. Biomed. Imaging: Macro to Nano, vols. 1 and 2, pp. 660–663 (2004)Google Scholar
  3. 3.
    Guizar-Sicairos, M., Thurman, S.T., Fienup, J.R.: Efficient subpixel image registration algorithms. Optics Letters 33(2), 156–158 (2008)CrossRefGoogle Scholar
  4. 4.
    Joshi, N., Szeliski, R., Kriegman, D.: PSF estimation using sharp edge prediction. In: IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2008, pp. 1–8. IEEE (2008)Google Scholar
  5. 5.
    Khare, A., Tiwary, U.S., Jeon, M.: Daubechies complex wavelet transform based multilevel shrinkage for deblurring of medical images in presence of noise. International Journal of Wavelets, Multiresolution and Information Processing 7(05), 587–604 (2009)MATHCrossRefGoogle Scholar
  6. 6.
    Lindblad, J., Sladoje, N., Malm, P., Bengtsson, E., Moshavegh, R., Mehnert, A.: Optimizing optics and imaging for pattern recognition based screening tasks. In: Proc. Int. Conf. on Pattern Recogn., Stockholm, Sweden, pp. 3333–3338, August 2014Google Scholar
  7. 7.
    Malm, P.: Image Analysis in Support of Computer-Assisted Cervical Cancer Screening. Ph.D. thesis, Uppsala University, Department of Information Technology (2013)Google Scholar
  8. 8.
    Malm, P.: Multi-resolution cervical cell dataset. Tech. Rep. 37. Uppsala University, Division of Visual Information and Interaction (2013)Google Scholar
  9. 9.
    Oliveira, J.P., Bioucas-Dias, J.M., Figueiredo, M.A.: Adaptive total variation image deblurring: a majorization-minimization approach. Signal Processing 89(9), 1683–1693 (2009)MATHCrossRefGoogle Scholar
  10. 10.
    Osher, S., Burger, M., Goldfarb, D., Xu, J., Yin, W.: An iterative regularization method for total variation-based image restoration. Multiscale Modeling & Simulation 4(2), 460–489 (2005)MATHMathSciNetCrossRefGoogle Scholar
  11. 11.
    Reddy, B., Chatterji, B.N.: An FFT-based technique for translation, rotation, and scale-invariant image registration. IEEE Transactions on Image Processing 5(8), 1266–1271 (1996)CrossRefGoogle Scholar
  12. 12.
    Smith, E.H.B.: PSF estimation by gradient descent fit to the ESF. In: Electronic Imaging 2006, pp. 60590E–60590E. International Society for Optics and Photonics (2006)Google Scholar
  13. 13.
    Wang, Z., Bovik, A.C., Sheikh, H.R., Simoncelli, E.P.: Image quality assessment: From error visibility to structural similarity. IEEE Transactions on Image Processing 13(4), 600–612 (2004)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Joakim Lindblad
    • 1
  • Ewert Bengtsson
    • 2
  • Nataša Sladoje
    • 2
    • 3
  1. 1.Faculty of Technical SciencesUniversity of Novi SadNovi SadSerbia
  2. 2.Centre for Image Analysis, Department of Information TechnologyUppsala UniversityUppsalaSweden
  3. 3.Mathematical InstituteSerbian Academy of Sciences and ArtsBelgradeSerbia

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