Phase Contrast Image Restoration via Dictionary Representation of Diffraction Patterns

  • Hang Su
  • Zhaozheng Yin
  • Takeo Kanade
  • Seungil Huh
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7512)


The restoration of microscopy images makes the segmentation and detection of cells easier and more reliable, which facilitates automated cell tracking and cell behavior analysis. In this paper, the authors analyze the image formation process of phase contrast images and propose an image restoration method based on the dictionary representation of diffraction patterns. By formulating and solving a min-ℓ1 optimization problem, each pixel is restored into a feature vector corresponding to the dictionary representation. Cells in the images are then segmented by the feature vector clustering. In addition to segmentation, since the feature vectors capture the information on the phase retardation caused by cells, they can be used for cell stage classification between intermitotic and mitotic/apoptotic stages. Experiments on three image sequences demonstrate that the dictionary-based restoration method can restore phase contrast images containing cells with different optical natures and provide promising results on cell stage classification.


Feature Vector Phase Retardation Phase Contrast Image Cell Segmentation Bright Cell 
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-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Hang Su
    • 1
    • 3
  • Zhaozheng Yin
    • 2
  • Takeo Kanade
    • 3
  • Seungil Huh
    • 3
  1. 1.Department of EEShanghai Jiaotong UniversityChina
  2. 2.Department of CSMissouri University of Science and TechnologyUSA
  3. 3.The Robotics InstituteCarnegie Mellon UniversityUSA

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