, Volume 3, Issue 4, pp 403–414 | Cite as

Empowering Low-Cost CMOS Cameras by Image Processing to Reach Comparable Results with Costly CCDs

  • Gözen Köklü
  • Julien Ghaye
  • Ralph Etienne-Cummings
  • Yusuf Leblebici
  • Giovanni De Micheli
  • Sandro Carrara


Despite the huge research effort to improve the performance of the complementary metal oxide semiconductor (CMOS) image sensors, charge-coupled devices (CCDs) still dominate the cell biology-related conventional fluorescence microscopic imaging market where low or ultra-low noise imaging is required. A detailed comparison of the sensor specifications and performance is usually not provided by the manufacturers which leads the end users not to go out of the habitude and choose a CCD camera instead of a CMOS one. However, depending on the application, CMOS cameras, when empowered by image processing algorithms, can become cost-efficient solutions for conventional fluorescence microscopy. In this paper, we introduce an application-based comparative study between the default CCD camera of an inverted microscope (Nikon Ti-S Eclipse) and a custom-designed CMOS camera and apply efficient image processing algorithms to improve the performance of CMOS cameras. Quantum micro-bead samples (emitting fluorescence light at different intensity levels), breast cancer diagnostic tissue cell samples, and Caco-2 cell samples are imaged by both CMOS and CCD cameras. The results are provided to show the reliability of CMOS camera processed images and finally to be of assistance when scientists select their cameras for desired applications.


Fluorescence microscopy cameras CMOS camera CCD camera CCD vs CMOS CMOS image sensor CCD image sensor 



The research work presented in this paper was funded by the NutriCHIP project with a grant from the Swiss initiative, evaluated by the Swiss National Science Foundation. It was also partially supported by the NanoSys project, in the program ERC-2009-AdG-246810. Finally, the authors would like to thank to Ata Tuna Çiftlik from LMIS2 (Microsystems Laboratory 2), EPFL, for their support in tissue sample preparation; and Ali Galip Bayrak from LAP (Processor Architecture Laboratory), EPFL, for his precious suggestions and the useful discussions.


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Copyright information

© Springer Science+Business Media New York 2013

Authors and Affiliations

  • Gözen Köklü
    • 1
    • 2
  • Julien Ghaye
    • 1
  • Ralph Etienne-Cummings
    • 3
  • Yusuf Leblebici
    • 2
  • Giovanni De Micheli
    • 1
  • Sandro Carrara
    • 1
  1. 1.Integrated Systems Laboratory (LSI)Swiss Federal Institute of TechnologyLausanneSwitzerland
  2. 2.Microelectronic Systems Laboratory (LSM)Swiss Federal Institute of TechnologyLausanneSwitzerland
  3. 3.Computational Sensory-Motor Systems LabJohns Hopkins UniversityBaltimoreUSA

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