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Iterative illumination correction with implicit regularization

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Abstract

This paper presents a retrospective algorithm for correcting the uneven illumination field in microscopy images. The illumination field is iteratively made uniform using an increasing sequence of bivariate polynomials. At each iteration, the least squares problem of fitting a 2-D polynomial to a sampled image is solved by using QR decomposition with column pivoting, where image samples are obtained by dynamic programming or watershed transform. This incremental scheme allows the smoothness constraint of the estimated bias field to be implicitly satisfied. The proper number of iterations is determined by an automatic stopping criterion. The experimental results show the effectiveness of the proposed approach when compared to a set of different well-established methods.

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References

  1. Al-Tam, F., dos Anjos, A., Bellafiore, S., Shahbazkia, H.R.: Detection of root knot nematodes in microscopy images. In: Proceedings of the International Conference on Bioimaging, pp. 76–81 (2015)

  2. Ali, R., Gooding, M., Szilágyi, T., Vojnovic, B., Christlieb, M., Brady, M.: Automatic segmentation of adherent biological cell boundaries and nuclei from brightfield microscopy images. Mach. Vis. Appl. 23(4), 607–621 (2012)

    Article  Google Scholar 

  3. Bansal, R., Staib, L.H., Peterson, B.S.: Correcting nonuniformities in mri intensities using entropy minimization based on an elastic model. In: Medical Image Computing and Computer-Assisted Intervention–MICCAI 2004, pp. 78–86. Springer (2004)

  4. Candes, E.J., Wakin, M.B., Boyd, S.P.: Enhancing sparsity by reweighted \(\ell _1\) minimization. J. Fourier Anal. Appl. 14(5–6), 877–905 (2008)

    Article  MathSciNet  MATH  Google Scholar 

  5. Chen, T., Yin, W., Zhou, X.S., Comaniciu, D., Huang, T.S.: Illumination normalization for face recognition and uneven background correction using total variation based image models. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2005. CVPR 2005, vol. 2, pp. 532–539. IEEE (2005)

  6. Cormen, T.H., Leiserson, C.E., Rivest, R.L., Stein, C., et al.: Introduction to algorithms, vol. 2. MIT press, Cambridge (2001)

    MATH  Google Scholar 

  7. Cruz-Roa, A., Caicedo, J.C., González, F.A.: Visual pattern mining in histology image collections using bag of features. Artifi. Intell. Med. 52(2), 91–106 (2011)

    Article  Google Scholar 

  8. dos Anjos, A., Møller, A.L., Ersbøll, B.K., Finnie, C., Shahbazkia, H.R.: New approach for segmentation and quantification of two-dimensional gel electrophoresis images. Bioinformatics 27(3), 368–375 (2011)

    Article  Google Scholar 

  9. Golub, G., Van Loan, C.: Matrix Computations. Johns Hopkins University Press, Baltimore (1996)

    MATH  Google Scholar 

  10. Gonzalez, R.C., Woods, R.E.: Digital Image Processing, 2nd edn. Prentice Hall, Upper Saddle River (2002)

    Google Scholar 

  11. Hast, A., Marchetti, A.: Improved illumination correction that preserves medium sized objects. Mach. Graph. Vis. 23, 3–20 (2014)

    Google Scholar 

  12. Heath, M.: Scientific Computing: An Introductory Survey. McGraw-Hill, New York (2002)

    MATH  Google Scholar 

  13. Kubecka, L., Jan, J., Kolar, R.: Retrospective illumination correction of retinal images. Int. J. Biomed. Imaging 2010 (2010). doi:10.1155/2010/780262

  14. Leahy, C., O’Brien, A., Dainty, C.: Illumination correction of retinal images using laplace interpolation. Appl. Opt. 51(35), 8383–8389 (2012)

    Article  Google Scholar 

  15. Lefkimmiatis, S., Bourquard, A., Unser, M.: Hessian-based norm regularization for image restoration with biomedical applications. Image Process. IEEE Trans. 21(3), 983–995 (2012)

    Article  MathSciNet  Google Scholar 

  16. Li, C., Gatenby, C., Wang, L., Gore, J.C.: A robust parametric method for bias field estimation and segmentation of MR images. In: IEEE Conference on Computer Vision and Pattern Recognition, 2009. CVPR 2009, pp. 218–223. IEEE (2009)

  17. Likar, B., Maintz, J.A., Viergever, M.A., Pernus, F., et al.: Retrospective shading correction based on entropy minimization. J. Microsc. 197(3), 285–295 (2000)

    Article  Google Scholar 

  18. Lindblad, J., Bengtsson, E.: A comparison of methods for estimation of intensity non-uniformities in 2D and 3D microscope images of fluorescence stained cells. In: Proceedings of the 12th Scandinavian Conference on Image Analysis (SCIA), pp. 264–271 (2001)

  19. Ma, W., Morel, J.M., Osher, S., Chien, A.: An L 1-based variational model for Retinex theory and its application to medical images. In: 2011 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 153–160. IEEE (2011)

  20. Olshausen, B.A., Field, D.J.: Natural image statistics and efficient coding*. Netw. Comput. Neural Syst. 7(2), 333–339 (1996)

  21. Rambabu, C., Chakrabarti, I.: An efficient immersion-based watershed transform method and its prototype architecture. J. Syst. Archit. 53(4), 210–226 (2007)

    Article  Google Scholar 

  22. Samsonov, A., Whitaker, R., Kholmovski, E., Johnson, C.: Parametric method for correction of intensity inhomogeneity in MRI data. p. 154 (2002)

  23. Shamir, L., Orlov, N., Eckley, D.M., Macura, T., Goldberg, I.: Iicbu 2008: a proposed benchmark suite for biological image analysis. Med. Biol. Eng. Comput. 46(9), 943–947 (2008)

    Article  Google Scholar 

  24. Sled, J., Zijdenbos, A., Evans, A.: A nonparametric method for automatic correction of intensity nonuniformity in mri data. Med. Imaging IEEE Trans. 17(1), 87–97 (1998)

    Article  Google Scholar 

  25. Tasdizen, T., Jurrus, E., Whitaker, R.T.: Non-uniform illumination correction in transmission electron microscopy. In: MICCAI Workshop on Microscopic Image Analysis with Applications in Biology, pp. 5–6 (2008)

  26. Tomaževič, D., Likar, B., Pernuš, F.: Comparative evaluation of retrospective shading correction methods. J. Microsc. 208(3), 212–223 (2002)

    Article  MathSciNet  MATH  Google Scholar 

  27. Vincent, L., Soille, P.: Watersheds in digital spaces: an efficient algorithm based on immersion simulations. Pattern Anal. Mach. Intell. IEEE Trans. 13(6), 583–598 (1991)

    Article  Google Scholar 

  28. Vovk, U., Pernus, F., Likar, B.: A review of methods for correction of intensity inhomogeneity in MRI. Med. Imaging IEEE Trans. 26(3), 405–421 (2007)

    Article  Google Scholar 

  29. Young, I.T.: Shading Correction: Compensation for Illumination and Sensor Inhomogeneities. Wiley, Hoboken (2001)

    Google Scholar 

  30. Zheng, Y., Gee, J.C.: Estimation of image bias field with sparsity constraints. In: 2010 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 255–262. IEEE (2010)

  31. Zheng, Y., Grossman, M., Awate, S.P., Gee, J.C.: Automatic correction of intensity nonuniformity from sparseness of gradient distribution in medical images. In: Yang, G.-Z., Hawkes, D., Rueckert, D., Noble, A., Taylor, C. (eds.) Medical Image Computing and Computer-Assisted Intervention-MICCAI 2009, pp. 852–859. Springer, Berlin, Heidelberg (2009)

  32. Zheng, Y., Lin, S., Kambhamettu, C., Yu, J., Kang, S.B.: Single-image vignetting correction. Pattern Anal. Mach. Intell. IEEE Trans. 31(12), 2243–2256 (2009)

    Article  Google Scholar 

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Acknowledgments

The authors are grateful to Thamar University, Dhamar-Yemen, Infectiopôle Sud, Marseille-France, and the Bill & Melinda Gates foundation.

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Authors and Affiliations

  1. FCT, Departamento de Engenharia Electrónica e Informática, Universidade do Algarve, 8005-139, Faro, Portugal

    Faroq Al-Tam & Hamid Reza Shahbazkia

  2. DCT, Instituto Superior Manuel Teixeira Gomes, 8500-508, Portimão, Portugal

    António dos Anjos

  3. CCMAR, Universidade do Algarve, 8005-139, Faro, Portugal

    António dos Anjos

Authors
  1. Faroq Al-Tam
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  2. António dos Anjos
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  3. Hamid Reza Shahbazkia
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Correspondence to Faroq Al-Tam.

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Al-Tam, F., dos Anjos, A. & Shahbazkia, H.R. Iterative illumination correction with implicit regularization. SIViP 10, 967–974 (2016). https://doi.org/10.1007/s11760-015-0847-4

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  • DOI: https://doi.org/10.1007/s11760-015-0847-4

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