Abstract
Many microscopic imaging modalities suffer from the problem of intensity inhomogeneity due to uneven illumination or camera nonlinearity, known as shading artifacts. A typical example of this is the unwanted seam when stitching images to obtain a whole slide image (WSI). Elimination of shading plays an essential role for subsequent image processing such as segmentation, registration, or tracking. In this paper, we propose two new retrospective shading correction algorithms for WSI targeted to two common forms of WSI: multiple image tiles before mosaicking and an already-stitched image. Both methods leverage on recent achievements in matrix rank minimization and sparse signal recovery. We show how the classic shading problem in microscopy can be reformulated as a decomposition problem of low-rank and sparse components, which seeks an optimal separation of the foreground objects of interest and the background illumination field. Additionally, a sparse constraint is introduced in the Fourier domain to ensure the smoothness of the recovered background. Extensive qualitative and quantitative validation on both synthetic and real microscopy images demonstrates superior performance of the proposed methods in shading removal in comparison with a well-established method in ImageJ.
Chapter PDF
Similar content being viewed by others
Keywords
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.
References
Piccinini, F., Bevilacqua, A., Smith, K., Horvath, P.: Vignetting and photo-bleaching correction in automated fluorescence microscopy from an array of overlapping images. In: 2013 ISBI, pp. 464–467. IEEE (2013)
Tomazevic, D., Likar, B., Pernus, F.: Comparative evaluation of retrospective shading correction methods. J. Microsc. 208, 212–223 (2002)
Sternberg, S.R.: Biomedical Image Processing. IEEE Comput. 16, 22–34 (1983)
Leong, F.J.W.-M., Brady, M., McGee, J.O.: Correction of uneven illumination (vignetting) in digital microscopy images. J. Clin. Pathol. 56, 619–621 (2003)
Likar, B., Maintz, J.B., Viergever, M.A., Pernus, F.: Retrospective shading correction based on entropy minimization. J. Microsc. 197, 285–295 (2000)
Russ, J.C.: The Image Processing Handbook, 6th edn. CRC Press (2011)
Cand, E.J., Li, X., Ma, Y., Wright, J.: Robust Principal Component Analysis? (2009)
Lin, Z., Chen, M., Ma, Y.: The Augmented Lagrange Multiplier Method for Exact Recovery of Corrupted Low-Rank Matrices. In: NIPS (2011)
Liang, X., Ren, X., Zhang, Z., Ma, Y.: Repairing sparse low-rank texture. In: Fitzgibbon, A., Lazebnik, S., Perona, P., Sato, Y., Schmid, C. (eds.) ECCV 2012, Part V. LNCS, vol. 7576, pp. 482–495. Springer, Heidelberg (2012)
Collins, T.J.: ImageJ for microscopy. Biotechniques 43, 25–30 (2007)
Babaloukas, G., Tentolouris, N., Liatis, S., Sklavounou, A., Perrea, D.: Evaluation of three methods for retrospective correction of vignetting on medical microscopy images utilizing two open source software tools. J. Microsc. 244, 320–324 (2011)
Piccinini, F., Lucarelli, E., Gherardi, A., Bevilacqua, A.: Multi-image based method to correct vignetting effect in light microscopy images. J. Microsc. 248, 6–22 (2012)
Lehmussola, A., Ruusuvuori, P., Selinummi, J., Huttunen, H., Yli-Harja, O.: Computational framework for simulating fluorescence microscope images with cell populations. IEEE Trans. Med. Imaging 26, 1010–1016 (2007)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 Springer International Publishing Switzerland
About this paper
Cite this paper
Peng, T., Wang, L., Bayer, C., Conjeti, S., Baust, M., Navab, N. (2014). Shading Correction for Whole Slide Image Using Low Rank and Sparse Decomposition. In: Golland, P., Hata, N., Barillot, C., Hornegger, J., Howe, R. (eds) Medical Image Computing and Computer-Assisted Intervention – MICCAI 2014. MICCAI 2014. Lecture Notes in Computer Science, vol 8673. Springer, Cham. https://doi.org/10.1007/978-3-319-10404-1_5
Download citation
DOI: https://doi.org/10.1007/978-3-319-10404-1_5
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-10403-4
Online ISBN: 978-3-319-10404-1
eBook Packages: Computer ScienceComputer Science (R0)