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Shading Correction for Whole Slide Image Using Low Rank and Sparse Decomposition

  • Tingying Peng
  • Lichao Wang
  • Christine Bayer
  • Sailesh Conjeti
  • Maximilian Baust
  • Nassir Navab
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8673)

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.

Keywords

Discrete Cosine Transform Foreground Object Sparse Constraint Intensity Inhomogeneity Whole Slide Image 
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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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Tingying Peng
    • 1
    • 3
  • Lichao Wang
    • 1
    • 4
  • Christine Bayer
    • 5
  • Sailesh Conjeti
    • 1
    • 6
  • Maximilian Baust
    • 1
  • Nassir Navab
    • 1
    • 2
  1. 1.Computer Aided Medical Procedures (CAMP)Technische Universität MünchenGermany
  2. 2.Computer Aided Medical Procedures (CAMP)Johns Hopkins UniversityUSA
  3. 3.Department of Nuclear MedicineTechnische Universität MünchenGermany
  4. 4.Helmholtz Zentrum MünchenGermany
  5. 5.Department of Radiation OncologyTechnische Universität MünchenGermany
  6. 6.School of Medical Science and TechnologyIIT KharagpurIndia

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